International Journal of Image, Graphics and Signal Processing (IJIGSP)

ISSN: 2074-9074 (Print)

ISSN: 2074-9082 (Online)

DOI: https://doi.org/10.5815/ijigsp

Website: https://www.mecs-press.org/ijigsp

Published By: MECS Press

Frequency: 6 issues per year

Number(s) Available: 129

ICV: 2014 8.22

(IJIGSP) in Google Scholar Citations / h5-index

IJIGSP is committed to bridge the theory and practice of images, graphics, and signal processing. From innovative ideas to specific algorithms and full system implementations, IJIGSP publishes original, peer-reviewed, and high quality articles in the areas of images, graphics, and signal processing. IJIGSP is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of images, graphics, and signal processing applications.

 

IJIGSP has been abstracted or indexed by several world class databases: Scopus, Google Scholar, Microsoft Academic Search, CrossRef, Baidu Wenku, IndexCopernicus, IET Inspec, EBSCO, JournalSeek, ULRICH's Periodicals Directory, WorldCat, Scirus, Academic Journals Database, Stanford University Libraries, Cornell University Library, UniSA Library, CNKI Scholar, ProQuest, J-Gate, ZDB, BASE, OhioLINK, iThenticate, Open Access Articles, Open Science Directory, National Science Library of Chinese Academy of Sciences, The HKU Scholars Hub, etc..

Latest Issue
Most Viewed
Most Downloaded

IJIGSP Vol. 16, No. 1, Feb. 2024

REGULAR PAPERS

A Survey of Artificial Life and Nature-inspired Techniques in Computer Graphics and Visualization

By Bushra Ferdousi Tim Mc Graw

DOI: https://doi.org/10.5815/ijigsp.2024.01.01, Pub. Date: 8 Feb. 2024

Artificial life and other nature-inspired techniques have been applied to many problems in computer graphics. Some of these techniques are based on observations of organic systems, such as slime molds and flocking animals, and can mimic some of their behaviors and structures. The emergent behavior of these systems can improve the realism of procedurally-generated assets used in computer graphics applications, such as animation and texture maps. In this work, we provide a survey of these techniques and applications, including cellular automata, differential growth, reaction-diffusion, and Physarum. The techniques are compared and contrasted, and the common themes and patterns are elucidated to create a taxonomy which can be useful to researchers studying existing techniques and developing new ones.

[...] Read more.
Algorithms for Polarization-singular processing of Mueller-matrix images of Soft Tissues for Biomedical Applications

By Liliya Diachenko Edgar Vatamanitsa Oleksandr Ushenko Oleksandr Salega Oleksandra Litvinenko Zhengbing Hu

DOI: https://doi.org/10.5815/ijigsp.2024.01.02, Pub. Date: 8 Feb. 2024

Traditional methods of imaging Muller-matrix polarimetry ensure obtaining large arrays of experimental data in the form of 16 Muller-matrix images. Processing and comparative analysis of the received information is quite time-consuming and requires a long time. A new algorithmic polarization-singular approach to the analysis of coordinate distributions of matrix elements (Mueller-matrix maps) of polycrystalline birefringent structure of biological tissues is considered. A Mueller-matrix model for describing the optical anisotropy of biological layers is proposed. Analytical correlations between polarization-singular states of the object field and characteristic values of Mueller-matrix images of birefringence soft tissue objects were found. The proposed algorithmic polarization-singular theory is experimentally verified. Examples of polarization singularities networks of Mueller-matrix maps of histological preparations of real tissues of female reproductive sphere are given. Diagnostic possibilities of the developed polarization-singular algorithms in diagnostics and differentiation of the stages of extragenital endometriosis are illustrated. Another area of biomedical diagnostics has been successfully tested: polarization-singular criteria for forensic Mueller-matrix determination of the age of myocardial injury of the deceased have been defined.

[...] Read more.
Performance Analysis of Various Image Feature Extractor Filters for Pothole Anomaly Classification

By Risikat Folashade Adebiyi Habeeb Bello-Salau Adeiza James Onumanyi Bashir Olaniyi Sadiq Abdulfatai Dare Adekale Busayo Hadir Adebiyi Emmanuel Adewale Adedokun

DOI: https://doi.org/10.5815/ijigsp.2024.01.03, Pub. Date: 8 Feb. 2024

Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the other hand, has an accuracy of roughly 87% when it comes to classifying potholes in low-light conditions. These findings are crucial in the selection of feature extraction filters for use by ML classifiers in the development of a robust autonomous pothole detection and classification system for improved navigation on anomalous roads and possible integration into self-driving cars.

[...] Read more.
Copy-Move Forgery Detection and Localization Framework for Images Using Stationary Wavelet Transform and Hybrid Dilated Adaptive VGG16 with Optimization Strategy

By Prabhu Bevinamarad Prakash Unki Padmaraj Nidagundi

DOI: https://doi.org/10.5815/ijigsp.2024.01.04, Pub. Date: 8 Feb. 2024

Due to the availability of low-cost electronic devices and advanced image editing tools, changing the semantic meaning of a particular image has become straightforward by employing various image manipulation techniques like image copy-move, image splicing and removal operations. The tampered images with this sophisticated software are rich in visualization, making the modifications invisible to the naked eye. Detecting these image alterations is laborious, time-consuming, and often yields inappropriate results. The current techniques use conventional square, slide regular, and artifacts procedures to identify image deviations to combat image forgery practices. Still, these techniques exhibit problems related to generalization, training and testing, and model complexity. So, in this paper, a novel image forgery detection and localization framework is implemented using stationary wavelet transform (SWT), and a Hybrid Dilated Adaptive VGG16 model with optimization is introduced to classify forgery images and localize the forgery regions present in an image. Initially, the proposed framework processes the input image with SWT to decompose an image into different subband and further divide it into patches. After that, the hybrid dilated adaptive VGG16 Network (HDA-VGG16Net) is built to extract the deep image features from the patches. Later, the Hybridized Tuna Swarm with Bald Eagle Search Optimization (HTS-BESO) technique is applied to optimize the VGG16 parameters. Finally, feature matching is formed using multi-similarity searching to recognize whether the input image is forged or original by locating forgery regions. The evaluation results are compared with existing forgery detection approaches to ensure the efficiency of the developed model by considering multiple performance measures.

[...] Read more.
When Handcrafted Features Meet Deep Features: An Empirical Study on Component-Level Image Classification

By Tauseef Khan Ayatullah Faruk Mollah

DOI: https://doi.org/10.5815/ijigsp.2024.01.05, Pub. Date: 8 Feb. 2024

Scene text detection from natural images has been a prime focus from last few decades. Classification of foreground object components is an essential task in many scene text detection approaches under uncontrollable environment. As it heavily relies upon robust and discriminating features, several features have been engineered for component-level text non-text classification. Competency of such feature descriptors particularly in respect of deep features needs to be examined. In this paper, we present prospective feature descriptors applicable to component-level text non-text classification and examine their performance along with convolutional neural network based deep features. Series of experiments have been carried out on publicly available benchmark dataset(s) of multi-script document-type, scene-type, and combined text vs. non-text components. Interestingly, feature combination is found to put well-demonstrated deep features into tough competition on most datasets under consideration. For instance, on the combined text non-text classification problem, CNN based deep features yield 97.6%, whereas aggregated features produce an accuracy of 98.4%. Similar findings are obtained on other experiments as well. Along with the quantitative figures, results have been analyzed and insightful discussion is made to ascertain the conjectures drawn herein. This study may cater the need of leveraging potentially strong handcrafted feature descriptors.

[...] Read more.
Non-Invasive Blood Group Prediction Using Optimized EfficientNet Architecture: A Systematic Approach

By Nitin Sakharam Ujgare Nagendra Pratap Singh Prem Kumari Verma Madhusudan Patil Aryan Verma

DOI: https://doi.org/10.5815/ijigsp.2024.01.06, Pub. Date: 8 Feb. 2024

This research work proposed a non-invasive blood group prediction approach using deep learning. The ability to swiftly and accurately determine blood types plays a critical role in medical emergencies prior to administering red blood cell, platelet, and plasma transfusions. Even a minor error during blood transfer can have severe consequences, including fatality. Traditional methods rely on time-consuming automated blood analyzers for pathological assessment. However, these processes involve skin pricking, which can cause bleeding, fainting, and potential skin lacerations. The proposed approach circumvents noninvasive procedures by leveraging rich EfficientNet deep learning architecture to analyze images of superficial blood vessels found on the finger. By illuminating the finger with laser light, the optical image of blood vessels hidden on the finger skin surface area is captured, which incorporates specific antigen shapes such as antigen ‘A’ and antigen ‘B’ present on the surface. Captured shapes of different antigen further used to predict the blood group of humans. The system requires high-definition camera to capture the antigen pattern from the red blood cells surface for classification of blood type without piercing the skin of patient. The proposed solution is not only straightforward and easily implementable but also offers significant advantages in terms of cost-effectiveness and immediate identification of ABO blood groups. This approach holds great promise for medical emergencies, military battleground scenarios, and is particularly valuable when dealing with infants where invasive procedures pose additional risks.

[...] Read more.
Cyclone Prediction from Remote Sensing Images Using Hybrid Deep Learning Approach Based on AlexNet

By Harshal Patil

DOI: https://doi.org/10.5815/ijigsp.2024.01.07, Pub. Date: 8 Feb. 2024

With the world feeling the negative effects of climate change, detecting and predicting severe weather occurrences is now an extremely vital and difficult task. Cyclones, a type of extreme weather phenomenon, have increased in frequency and severity in Indian subcontinent regions over the past few years. It is estimated that around three cyclones struck the east coastal region of India, causing substantial damage to people, farms, and infrastructure. Predicting cyclones ahead of time is crucial for avoiding or significantly lowering the devastating effects. The traditional methodologies employed numerical equations that demand strong experience and greater skills to obtain satisfactory prediction accuracy. Problems with domain expertise and the probability of human mistakes can be avoided with the help of Deep Learning (DL). As a result, in this work, we sought to forecast cyclone intensity using a Convolution Neural Network (CNN), a basic DL structure. To increase the CNN model's architecture and effectiveness, hybrid models such as Convolution Neural Network & Long short-term memory (CNN-LSTM) and AlexNet & Gated recurrent units (AlexNet-GRU) are developed. Data from the INSAT 3D satellite was utilized to develop and evaluate the DL model. We processed both the training and testing dataset and increase the training dataset using augmentation. All three DL models are tested and compared, the AlexNet-GRU model outperforms on the test data, with a relatively high accuracy of 93.35% and a low mean square error (MSE) of 215.

[...] Read more.
Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

By Ishwari Singh Rajput Sonam Tyagi Aditya Gupta Vibha Jain

DOI: https://doi.org/10.5815/ijigsp.2024.01.08, Pub. Date: 8 Feb. 2024

White blood cells (WBC) perform a vital function within the immune system by actively protecting the body from a wide range of diseases and foreign substances. Diverse types of WBCs exist, including neutrophils, lymphocytes, eosinophils, and monocytes, each possessing distinct roles within the immune response. Neutrophils are typically the initial immune cells to mobilize in response to infections and inflammation, exhibiting a rapid and robust reaction. Conversely, lymphocytes play a pivotal role in the recognition and targeted elimination of pathogens. Nevertheless, identifying and classifying WBCs poses significant challenges and demands considerable time, even for seasoned medical practitioners. The process of manual classification is frequently characterized by subjectivity and is susceptible to errors, thereby potentially compromising the precision of both diagnosis and treatment. In response to this challenge, scholars have devised deep learning methodologies that can automate the process of WBC classification, thereby enhancing its precision. This study employs a convolutional neural network (CNN) to classify WBCs based on imaging data. The CNN underwent training using a substantial dataset comprising body cell images. This training facilitated the acquisition of discerning characteristics specific to various WBC types, thereby enabling accurate classification. The methodology was evaluated within a simulated environment, yielding encouraging outcomes. The approach that was proposed successfully achieved an average accuracy rate of 98.33% in the classification of WBCs. This outcome serves as evidence of deep learning techniques enhancing the speed and accuracy of WBC classification.

[...] Read more.
Text Region Extraction: A Morphological Based Image Analysis Using Genetic Algorithm

By Dhirendra Pal Singh Ashish Khare

DOI: https://doi.org/10.5815/ijigsp.2015.02.06, Pub. Date: 8 Jan. 2015

Image analysis belongs to the area of computer vision and pattern recognition. These areas are also a part of digital image processing, where researchers have a great attention in the area of content retrieval information from various types of images having complex background, low contrast background or multi-spectral background etc. These contents may be found in any form like texture data, shape, and objects. Text Region Extraction as a content from an mage is a class of problems in Digital Image Processing Applications that aims to provides necessary information which are widely used in many fields medical imaging, pattern recognition, Robotics, Artificial intelligent Transport systems etc. To extract the text data information has becomes a challenging task. Since, Text extraction are very useful for identifying and analysis the whole information about image, Therefore, In this paper, we propose a unified framework by combining morphological operations and Genetic Algorithms for extracting and analyzing the text data region which may be embedded in an image by means of variety of texts: font, size, skew angle, distortion by slant and tilt, shape of the object which texts are on, etc. We have established our proposed methods on gray level image sets and make qualitative and quantitative comparisons with other existing methods and concluded that proposed method is better than others.

[...] Read more.
Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm

By Joseph Isabona Agbotiname Lucky Imoize Stephen Ojo

DOI: https://doi.org/10.5815/ijigsp.2023.05.01, Pub. Date: 8 Oct. 2023

Denoising is a vital aspect of image preprocessing, often explored to eliminate noise in an image to restore its proper characteristic formation and clarity. Unfortunately, noise often degrades the quality of valuable images, making them meaningless for practical applications. Several methods have been deployed to address this problem, but the quality of the recovered images still requires enhancement for efficient applications in practice. In this paper, a wavelet-based universal thresholding technique that possesses the capacity to optimally denoise highly degraded noisy images with both uniform and non-uniform variations in illumination and contrast is proposed. The proposed method, herein referred to as the modified wavelet-based universal thresholding (MWUT), compared to three state-of-the-art denoising techniques, was employed to denoise five noisy images. In order to appraise the qualities of the images obtained, seven performance indicators comprising the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Structural Content (SC), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Signal-to-Reconstruction-Error Ratio (SRER), Blind Spatial Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) were employed. The first five indicators – RMSE, MAE, SC, PSNR, SSIM, and SRER- are reference indicators, while the remaining two – NIQE and BRISQUE- are referenceless. For the superior performance of the proposed wavelet threshold algorithm, the SC, PSNR, SSIM, and SRER must be higher, while lower values of NIQE, BRISQUE, RMSE, and MAE are preferred. A higher and better value of PSNR, SSIM, and SRER in the final results shows the superior performance of our proposed MWUT denoising technique over the preliminaries. Lower NIQE, BRISQUE, RMSE, and MAE values also indicate higher and better image quality results using the proposed modified wavelet-based universal thresholding technique over the existing schemes. The modified wavelet-based universal thresholding technique would find practical applications in digital image processing and enhancement.

[...] Read more.
A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis

By Jiashu Xu

DOI: https://doi.org/10.5815/ijigsp.2021.04.03, Pub. Date: 8 Aug. 2021

In the field of medical image analysis, supervised deep learning strategies have achieved significant development, while these methods rely on large labeled datasets. Self-Supervised learning (SSL) provides a new strategy to pre-train a neural network with unlabeled data. This is a new unsupervised learning paradigm that has achieved significant breakthroughs in recent years. So, more and more researchers are trying to utilize SSL methods for medical image analysis, to meet the challenge of assembling large medical datasets. To our knowledge, so far there still a shortage of reviews of self-supervised learning methods in the field of medical image analysis, our work of this article aims to fill this gap and comprehensively review the application of self-supervised learning in the medical field. This article provides the latest and most detailed overview of self-supervised learning in the medical field and promotes the development of unsupervised learning in the field of medical imaging. These methods are divided into three categories: context-based, generation-based, and contrast-based, and then show the pros and cons of each category and evaluates their performance in downstream tasks. Finally, we conclude with the limitations of the current methods and discussed the future direction.

[...] Read more.
Radio Receiver with Internal Compression of Input Signals Using a Dispersive Delay Line with Bandpass Filters

By Roman Pantyeyev Felix Yanovsky Andriy Mykolushko Volodymyr Shutko

DOI: https://doi.org/10.5815/ijigsp.2023.06.01, Pub. Date: 8 Dec. 2023

This article proposes a receiving device in which arbitrary input signals are subject to pre-detector processing for the subsequent implementation of the idea of compressing broadband modulated pulses with a matched filter to increase the signal-to-noise ratio and improve resolution. For this purpose, a model of a dispersive delay line is developed based on series-connected high-frequency time delay lines with taps in the form of bandpass filters, and analysis of this model is performed as a part of the radio receiving device with chirp signal compression. The article presents the mathematical description of the processes of formation and compression of chirp signals based on their matched filtering using the developed model and proposes the block diagram of a radio receiving device using the principle of compression of received signals. The proposed model can be implemented in devices for receiving unknown signals, in particular in passive radar. It also can be used for studying signal compression processes based on linear frequency modulation in traditional radar systems.

[...] Read more.
Edibility Detection of Mushroom Using Ensemble Methods

By Nusrat Jahan Pinky S.M. Mohidul Islam Rafia Sharmin Alice

DOI: https://doi.org/10.5815/ijigsp.2019.04.05, Pub. Date: 8 Apr. 2019

Mushrooms are the most familiar delicious food which is cholesterol free as well as rich in vitamins and minerals. Though nearly 45,000 species of mushrooms have been known throughout the world, most of them are poisonous and few are lethally poisonous. Identifying edible or poisonous mushroom through the naked eye is quite difficult. Even there is no easy rule for edibility identification using machine learning methods that work for all types of data. Our aim is to find a robust method for identifying mushrooms edibility with better performance than existing works. In this paper, three ensemble methods are used to detect the edibility of mushrooms: Bagging, Boosting, and random forest. By using the most significant features, five feature sets are made for making five base models of each ensemble method. The accuracy is measured for ensemble methods using five both fixed feature set-based models and randomly selected feature set based models, for two types of test sets. The result shows that better performance is obtained for methods made of fixed feature sets-based models than randomly selected feature set-based models. The highest accuracy is obtained for the proposed model-based random forest for both test sets.

[...] Read more.
A Review on Image Reconstruction through MRI k-Space Data

By Tanuj Kumar Jhamb Vinith Rejathalal V.K. Govindan

DOI: https://doi.org/10.5815/ijigsp.2015.07.06, Pub. Date: 8 Jun. 2015

Image reconstruction is the process of generating an image of an object from the signals captured by the scanning machine. Medical imaging is an interdisciplinary field combining physics, biology, mathematics and computational sciences. This paper provides a complete overview of image reconstruction process in MRI (Magnetic Resonance Imaging). It reviews the computational aspect of medical image reconstruction. MRI is one of the commonly used medical imaging techniques. The data collected by MRI scanner for image reconstruction is called the k-space data. For reconstructing an image from k-space data, there are various algorithms such as Homodyne algorithm, Zero Filling method, Dictionary Learning, and Projections onto Convex Set method. All the characteristics of k-space data and MRI data collection technique are reviewed in detail. The algorithms used for image reconstruction discussed in detail along with their pros and cons. Various modern magnetic resonance imaging techniques like functional MRI, diffusion MRI have also been introduced. The concepts of classical techniques like Expectation Maximization, Sensitive Encoding, Level Set Method, and the recent techniques such as Alternating Minimization, Signal Modeling, and Sphere Shaped Support Vector Machine are also reviewed. It is observed that most of these techniques enhance the gradient encoding and reduce the scanning time. Classical algorithms provide undesirable blurring effect when the degree of phase variation is high in partial k-space. Modern reconstructions algorithms such as Dictionary learning works well even with high phase variation as these are iterative procedures.

[...] Read more.
Real-Time Vehicle Detection for Surveillance of River Dredging Areas Using Convolutional Neural Networks

By Mohammed Abduljabbar Zaid Al Bayati Muhammet Cakmak

DOI: https://doi.org/10.5815/ijigsp.2023.05.02, Pub. Date: 8 Oct. 2023

The presence of illegal activities such as illegitimate mining and sand theft in river dredging areas leads to economic losses. However, manual monitoring is expensive and time-consuming. Therefore, automated surveillance systems are preferred to mitigate such activities, as they are accurate and available at all times. In order to monitor river dredging areas, two essential steps for surveillance are vehicle detection and license plate recognition. Most current frameworks for vehicle detection employ plain feed-forward Convolutional Neural Networks (CNNs) as backbone architectures. However, these are scale-sensitive and cannot handle variations in vehicles' scales in consecutive video frames. To address these issues, Scale Invariant Hybrid Convolutional Neural Network (SIH-CNN) architecture is proposed for real-time vehicle detection in this study. The publicly available benchmark UA-DETRAC is used to validate the performance of the proposed architecture. Results show that the proposed SIH-CNN model achieved a mean average precision (mAP) of 77.76% on the UA-DETRAC benchmark, which is 3.94% higher than the baseline detector with real-time performance of 48.4 frames per seconds.

[...] Read more.
Fast Encryption Scheme for Secure Transmission of e-Healthcare Images

By Devisha Tiwari Bhaskar Mondal Anil Singh

DOI: https://doi.org/10.5815/ijigsp.2023.05.07, Pub. Date: 8 Oct. 2023

E-healthcare systems (EHSD), medical communications, digital imaging (DICOM) things have gained popularity over the past decade as they have become the top contenders for interoperability and adoption as a global standard for transmitting and communicating medical data. Security is a growing issue as EHSD and DICOM have grown more usable on any-to-any devices. The goal of this research is to create a privacy-preserving encryption technique for EHSD rapid communication with minimal storage. A new 2D logistic-sine chaotic map (2DLSCM) is used to design the proposed encryption method, which has been developed specifically for peer-to-peer communications via unique keys. Through the 3D Lorenz map which feeds the initial values to it, the 2DLSCM is able to provide a unique keyspace of 2544 bits (2^544bits) in each go of peer-to-peer paired transmission. Permutation-diffusion design is used in the encryption process, and 2DLSCM with 3DLorenz system are used to generate unique initial values for the keys. Without interfering with real-time medical transmission, the approach can quickly encrypt any EHSD image and DICOM objects. To assess the method, five distinct EHSD images of different kinds, sizes, and quality are selected. The findings indicate strong protection, speed, and scalability when compared to existing similar methods in literature.

[...] Read more.
Breast Cancer Classification from Ultrasound Images using VGG16 Model based Transfer Learning

By A. B. M. Aowlad Hossain Jannatul Kamrun Nisha Fatematuj Johora

DOI: https://doi.org/10.5815/ijigsp.2023.01.02, Pub. Date: 8 Feb. 2023

Ultrasound based breast screening is gaining attention recently especially for dense breast. The technological advancement, cancer awareness, and cost-safety-availability benefits lead rapid rise of breast ultrasound market. The irregular shape, intensity variation, and additional blood vessels of malignant cancer are distinguishable in ultrasound images from the benign phase. However, classification of breast cancer using ultrasound images is a difficult process owing to speckle noise and complex textures of breast. In this paper, a breast cancer classification method is presented using VGG16 model based transfer learning approach. We have used median filter to despeckle the images. The layers for convolution process of the pretrained VGG16 model along with the maxpooling layers have been used as feature extractor and a proposed fully connected two layers deep neural network has been designed as classifier. Adam optimizer is used with learning rate of 0.001 and binary cross-entropy is chosen as the loss function for model optimization. Dropout of hidden layers is used to avoid overfitting. Breast Ultrasound images from two databases (total 897 images) have been combined to train, validate and test the performance and generalization strength of the classifier. Experimental results showed the training accuracy as 98.2% and testing accuracy as 91% for blind testing data with a reduced of computational complexity. Gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions localization effort at the final convolutional layer and found as noteworthy. The outcomes of this work might be useful for the clinical applications of breast cancer diagnosis.

[...] Read more.
An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation

By Anam Mustaqeem Engr Ali Javed Tehseen Fatima

DOI: https://doi.org/10.5815/ijigsp.2012.10.05, Pub. Date: 28 Sep. 2012

During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this paper for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image.

[...] Read more.
Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm

By Joseph Isabona Agbotiname Lucky Imoize Stephen Ojo

DOI: https://doi.org/10.5815/ijigsp.2023.05.01, Pub. Date: 8 Oct. 2023

Denoising is a vital aspect of image preprocessing, often explored to eliminate noise in an image to restore its proper characteristic formation and clarity. Unfortunately, noise often degrades the quality of valuable images, making them meaningless for practical applications. Several methods have been deployed to address this problem, but the quality of the recovered images still requires enhancement for efficient applications in practice. In this paper, a wavelet-based universal thresholding technique that possesses the capacity to optimally denoise highly degraded noisy images with both uniform and non-uniform variations in illumination and contrast is proposed. The proposed method, herein referred to as the modified wavelet-based universal thresholding (MWUT), compared to three state-of-the-art denoising techniques, was employed to denoise five noisy images. In order to appraise the qualities of the images obtained, seven performance indicators comprising the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Structural Content (SC), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Signal-to-Reconstruction-Error Ratio (SRER), Blind Spatial Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) were employed. The first five indicators – RMSE, MAE, SC, PSNR, SSIM, and SRER- are reference indicators, while the remaining two – NIQE and BRISQUE- are referenceless. For the superior performance of the proposed wavelet threshold algorithm, the SC, PSNR, SSIM, and SRER must be higher, while lower values of NIQE, BRISQUE, RMSE, and MAE are preferred. A higher and better value of PSNR, SSIM, and SRER in the final results shows the superior performance of our proposed MWUT denoising technique over the preliminaries. Lower NIQE, BRISQUE, RMSE, and MAE values also indicate higher and better image quality results using the proposed modified wavelet-based universal thresholding technique over the existing schemes. The modified wavelet-based universal thresholding technique would find practical applications in digital image processing and enhancement.

[...] Read more.
Text Region Extraction: A Morphological Based Image Analysis Using Genetic Algorithm

By Dhirendra Pal Singh Ashish Khare

DOI: https://doi.org/10.5815/ijigsp.2015.02.06, Pub. Date: 8 Jan. 2015

Image analysis belongs to the area of computer vision and pattern recognition. These areas are also a part of digital image processing, where researchers have a great attention in the area of content retrieval information from various types of images having complex background, low contrast background or multi-spectral background etc. These contents may be found in any form like texture data, shape, and objects. Text Region Extraction as a content from an mage is a class of problems in Digital Image Processing Applications that aims to provides necessary information which are widely used in many fields medical imaging, pattern recognition, Robotics, Artificial intelligent Transport systems etc. To extract the text data information has becomes a challenging task. Since, Text extraction are very useful for identifying and analysis the whole information about image, Therefore, In this paper, we propose a unified framework by combining morphological operations and Genetic Algorithms for extracting and analyzing the text data region which may be embedded in an image by means of variety of texts: font, size, skew angle, distortion by slant and tilt, shape of the object which texts are on, etc. We have established our proposed methods on gray level image sets and make qualitative and quantitative comparisons with other existing methods and concluded that proposed method is better than others.

[...] Read more.
A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis

By Jiashu Xu

DOI: https://doi.org/10.5815/ijigsp.2021.04.03, Pub. Date: 8 Aug. 2021

In the field of medical image analysis, supervised deep learning strategies have achieved significant development, while these methods rely on large labeled datasets. Self-Supervised learning (SSL) provides a new strategy to pre-train a neural network with unlabeled data. This is a new unsupervised learning paradigm that has achieved significant breakthroughs in recent years. So, more and more researchers are trying to utilize SSL methods for medical image analysis, to meet the challenge of assembling large medical datasets. To our knowledge, so far there still a shortage of reviews of self-supervised learning methods in the field of medical image analysis, our work of this article aims to fill this gap and comprehensively review the application of self-supervised learning in the medical field. This article provides the latest and most detailed overview of self-supervised learning in the medical field and promotes the development of unsupervised learning in the field of medical imaging. These methods are divided into three categories: context-based, generation-based, and contrast-based, and then show the pros and cons of each category and evaluates their performance in downstream tasks. Finally, we conclude with the limitations of the current methods and discussed the future direction.

[...] Read more.
Breast Cancer Classification from Ultrasound Images using VGG16 Model based Transfer Learning

By A. B. M. Aowlad Hossain Jannatul Kamrun Nisha Fatematuj Johora

DOI: https://doi.org/10.5815/ijigsp.2023.01.02, Pub. Date: 8 Feb. 2023

Ultrasound based breast screening is gaining attention recently especially for dense breast. The technological advancement, cancer awareness, and cost-safety-availability benefits lead rapid rise of breast ultrasound market. The irregular shape, intensity variation, and additional blood vessels of malignant cancer are distinguishable in ultrasound images from the benign phase. However, classification of breast cancer using ultrasound images is a difficult process owing to speckle noise and complex textures of breast. In this paper, a breast cancer classification method is presented using VGG16 model based transfer learning approach. We have used median filter to despeckle the images. The layers for convolution process of the pretrained VGG16 model along with the maxpooling layers have been used as feature extractor and a proposed fully connected two layers deep neural network has been designed as classifier. Adam optimizer is used with learning rate of 0.001 and binary cross-entropy is chosen as the loss function for model optimization. Dropout of hidden layers is used to avoid overfitting. Breast Ultrasound images from two databases (total 897 images) have been combined to train, validate and test the performance and generalization strength of the classifier. Experimental results showed the training accuracy as 98.2% and testing accuracy as 91% for blind testing data with a reduced of computational complexity. Gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions localization effort at the final convolutional layer and found as noteworthy. The outcomes of this work might be useful for the clinical applications of breast cancer diagnosis.

[...] Read more.
Retinal Image Segmentation for Diabetic Retinopathy Detection using U-Net Architecture

By Swapnil V. Deshmukh Apash Roy Pratik Agrawal

DOI: https://doi.org/10.5815/ijigsp.2023.01.07, Pub. Date: 8 Feb. 2023

Diabetic retinopathy is one of the most serious eye diseases and can lead to permanent blindness if not diagnosed early. The main cause of this is diabetes. Not every diabetic will develop diabetic retinopathy, but the risk of developing diabetes is undeniable. This requires the early diagnosis of Diabetic retinopathy. Segmentation is one of the approaches which is useful for detecting the blood vessels in the retinal image. This paper proposed the three models based on a deep learning approach for recognizing blood vessels from retinal images using region-based segmentation techniques. The proposed model consists of four steps preprocessing, Augmentation, Model training, and Performance measure. The augmented retinal images are fed to the three models for training and finally, get the segmented image. The proposed three models are applied on publically available data set of DRIVE, STARE, and HRF. It is observed that more thin blood vessels are segmented on the retinal image in the HRF dataset using model-3. The performance of proposed three models is compare with other state-of-art-methods of blood vessels segmentation of DRIVE, STARE, and HRF datasets.

[...] Read more.
Edibility Detection of Mushroom Using Ensemble Methods

By Nusrat Jahan Pinky S.M. Mohidul Islam Rafia Sharmin Alice

DOI: https://doi.org/10.5815/ijigsp.2019.04.05, Pub. Date: 8 Apr. 2019

Mushrooms are the most familiar delicious food which is cholesterol free as well as rich in vitamins and minerals. Though nearly 45,000 species of mushrooms have been known throughout the world, most of them are poisonous and few are lethally poisonous. Identifying edible or poisonous mushroom through the naked eye is quite difficult. Even there is no easy rule for edibility identification using machine learning methods that work for all types of data. Our aim is to find a robust method for identifying mushrooms edibility with better performance than existing works. In this paper, three ensemble methods are used to detect the edibility of mushrooms: Bagging, Boosting, and random forest. By using the most significant features, five feature sets are made for making five base models of each ensemble method. The accuracy is measured for ensemble methods using five both fixed feature set-based models and randomly selected feature set based models, for two types of test sets. The result shows that better performance is obtained for methods made of fixed feature sets-based models than randomly selected feature set-based models. The highest accuracy is obtained for the proposed model-based random forest for both test sets.

[...] Read more.
A Review on Image Reconstruction through MRI k-Space Data

By Tanuj Kumar Jhamb Vinith Rejathalal V.K. Govindan

DOI: https://doi.org/10.5815/ijigsp.2015.07.06, Pub. Date: 8 Jun. 2015

Image reconstruction is the process of generating an image of an object from the signals captured by the scanning machine. Medical imaging is an interdisciplinary field combining physics, biology, mathematics and computational sciences. This paper provides a complete overview of image reconstruction process in MRI (Magnetic Resonance Imaging). It reviews the computational aspect of medical image reconstruction. MRI is one of the commonly used medical imaging techniques. The data collected by MRI scanner for image reconstruction is called the k-space data. For reconstructing an image from k-space data, there are various algorithms such as Homodyne algorithm, Zero Filling method, Dictionary Learning, and Projections onto Convex Set method. All the characteristics of k-space data and MRI data collection technique are reviewed in detail. The algorithms used for image reconstruction discussed in detail along with their pros and cons. Various modern magnetic resonance imaging techniques like functional MRI, diffusion MRI have also been introduced. The concepts of classical techniques like Expectation Maximization, Sensitive Encoding, Level Set Method, and the recent techniques such as Alternating Minimization, Signal Modeling, and Sphere Shaped Support Vector Machine are also reviewed. It is observed that most of these techniques enhance the gradient encoding and reduce the scanning time. Classical algorithms provide undesirable blurring effect when the degree of phase variation is high in partial k-space. Modern reconstructions algorithms such as Dictionary learning works well even with high phase variation as these are iterative procedures.

[...] Read more.
Algorithm of Processing Navigation Information in Systems of Quadrotor Motion Control

By Anatoly Tunik Olha Sushchenko Svitlana Ilnytska

DOI: https://doi.org/10.5815/ijigsp.2023.01.01, Pub. Date: 8 Feb. 2023

The article deals with creating an algorithm for processing information in a digital system for quadrotor flight control. The minimization of L2-gain using simple parametric optimization for the synthesis of the control algorithm based on static output feedback is proposed. The kinematical diagram and mathematical description of the linearized quadrotor model are represented. The transformation of the continuous model into a discrete one has been implemented. The new optimization procedure based on digital static output feedback is developed. Expressions for the optimization criterion and penalty function are given. The features of the creating algorithm and processing information are described. The development of the closed-loop control system with an extended model augmented with some essential nonlinearities inherent to the real control plant is implemented. The simulation of the quadrotor guidance in the turbulent atmosphere has been carried out. The simulation results based on the characteristics of the studied quadrotor are represented. These results prove the efficiency of the proposed algorithm for navigation information processing. The obtained results can be useful for signal processing and designing control systems for unmanned aerial vehicles of the wide class.

[...] Read more.
Real-Time Video based Human Suspicious Activity Recognition with Transfer Learning for Deep Learning

By Indhumathi .J Balasubramanian .M Balasaigayathri .B

DOI: https://doi.org/10.5815/ijigsp.2023.01.05, Pub. Date: 8 Feb. 2023

Nowadays, the primary concern of any society is providing safety to an individual. It is very hard to recognize the human behaviour and identify whether it is suspicious or normal. Deep learning approaches paved the way for the development of various machine learning and artificial intelligence. The proposed system detects real-time human activity using a convolutional neural network. The objective of the study is to develop a real-time application for Activity recognition using with and without transfer learning methods. The proposed system considers criminal, suspicious and normal categories of activities. Differentiate suspicious behaviour videos are collected from different peoples(men/women). This proposed system is used to detect suspicious activities of a person. The novel 2D-CNN, pre-trained VGG-16 and ResNet50 is trained on video frames of human activities such as normal and suspicious behaviour. Similarly, the transfer learning in VGG16 and ResNet50 is trained using human suspicious activity datasets. The results show that the novel 2D-CNN, VGG16, and ResNet50 without transfer learning achieve accuracy of 98.96%, 97.84%, and 99.03%, respectively. In Kaggle/real-time video, the proposed system employing 2D-CNN outperforms the pre-trained model VGG16. The trained model is used to classify the activity in the real-time captured video. The performance obtained on ResNet50 with transfer learning accuracy of 99.18% is higher than VGG16 transfer learning accuracy of 98.36%. 

[...] Read more.
Evolutionary Image Enhancement Using Multi-Objective Genetic Algorithm

By Dhirendra Pal Singh Ashish Khare

DOI: https://doi.org/10.5815/ijigsp.2014.01.09, Pub. Date: 8 Nov. 2013

Image Processing is the art of examining, identifying and judging the significances of the Images. Image enhancement refers to attenuation, or sharpening, of image features such as edgels, boundaries, or contrast to make the processed image more useful for analysis. Image enhancement procedures utilize the computers to provide good and improved images for study by the human interpreters. In this paper we proposed a novel method that uses the Genetic Algorithm with Multi-objective criteria to find more enhance version of images. The proposed method has been verified with benchmark images in Image Enhancement. The simple Genetic Algorithm may not explore much enough to find out more enhanced image. In the proposed method three objectives are taken in to consideration. They are intensity, entropy and number of edgels. Proposed algorithm achieved automatic image enhancement criteria by incorporating the objectives (intensity, entropy, edges). We review some of the existing Image Enhancement technique. We also compared the results of our algorithms with another Genetic Algorithm based techniques. We expect that further improvements can be achieved by incorporating linear relationship between some other techniques.

[...] Read more.