Shivanand S. Gornale

Work place: Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India



Research Interests: Information Retrieval, Image Processing, Pattern Recognition, Computer Vision, Computer systems and computational processes


Dr. Shivanand S. Gornale has completed M. Sc. in Computer Science. M.Phil. in Computer Science., Ph.D. in Computer Science from University of Pune ,Maharashtra, India in 2009 under the guidance of Dr. K V Kale and has been recognized as research guide for PhD in Computer Science and Engineering from Rani Channamma University, Belagavi and Jain University Bangalore. He has published more than 75 research papers in various National and Inter-national Journals and conferences. He is a Fellow of IETE New Delhi, Life Member of CSI, Life Member of Indian Unit of Pattern Recognition and Artificial Intelligence (IPRA), Member of Indian Association for Research in Computer Science (IARCS), Member of International Association of Computer Science and Information Technology (IACS&IT) Singapore, Member of International Association for Engineers’, Hong Kong, Member of Computer Science Teachers’ Association, USA, Life Member of Indian Science Congress Association, Kolkata-India. Presently he is working as Associate Professor and Chairman, Department of Computer Science, Rani Channamma University, Belagavi – Karnataka, India. His research area of interest is Digital Image Processing, Pattern Recognition, Computer Vision and Machine Learning, Video Retrieval and Biometric analysis.

Author Articles
Early Detection of Osteoarthritis based on Cartilage Thickness in Knee X-ray Images

By Shivanand S. Gornale Pooja U. Patravali P. S. Hiremath

DOI:, Pub. Date: 8 Sep. 2019

Arthritis is a joint disorder featuring inflammation. There are numerous forms of Arthritis. Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability. Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group. A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out. Analysis of bone structures in initial stage is bit complex. So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome. Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors. The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier. The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage. The results are competitive and promising as reported in the literature.

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Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

By Abhijit Patil Kruthi R Shivanand S. Gornale

DOI:, Pub. Date: 8 May 2019

A certain number of researchers have utilized uni-modal bio-metric traits for gender classification. It has many limitations which can be mitigated with inclusion of multiple sources of biometric information to identify or classify user’s information. Intuitively multimodal systems are more reliable and viable solution as multiple independent characteristics of modalities are fused together. The objective of this work is inferring the gender by combining different biometric traits like face, iris, and fingerprints of same subject. In the proposed work, feature level fusion is considered to obtain robustness in gender determination; and an accuracy of 99.8% was achieved on homologous multimodal biometric database SDUMLA-HMT (Group of Machine Learning and Applications, Shandong University). The results demonstrate that the feature level fusion of Multimodal Biometric system greatly improves the performance of gender classification and our approach outperforms the state-of-the-art techniques noticed in the literature.

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Analysis and Detection of Content based Video Retrieval

By Shivanand S. Gornale Ashvini K Babaleshwar Pravin L Yannawar

DOI:, Pub. Date: 8 Mar. 2019

Content Based Video Retrieval (CBVR) System has been investigated over past decade it’s rooted in many applications like developments and technologies. The demand for extraction of high level semantics contents as well as handling of low level contents in video retrieval systems are still in need. Hence it motivates and encourages many researchers to discover their knowledge across CBVR domain and contribute their work to make the system more effective and useful in developing the system application. This paper highlights comprehensive and extensive review of CBVR techniques for detection of region of interest in a given video. The experiment is carried out for the detection of ROI using ACF detector. The detection rate of ROI is observed competitive and satisfactory.

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Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images

By Shivanand S. Gornale Pooja U. Patravali Archana M. Uppin P. S. Hiremath

DOI:, Pub. Date: 8 Feb. 2019

Arthritis is one of the chronic joint disorders that have affected many lives including middle age and older age group. Arthritis exists in many forms and one among them is Osteoarthritis. Osteoarthritis affects the bigger joints like knee, hip, spine, feet etc. Early detection of Osteoarthritis is most essential if not treated properly may result in deformity. The researchers have become more concerned to detect the disorder in the early stage by merging their medical knowledge with machine vision approach in an appropriate way. The objective of this work is to study various segmentation techniques for the detection of Osteoarthritis in the early stage. The different segmentation technique like Sobel and Prewitt edge segmentation, Otsu’s method of segmentation and Texture based segmentation are used to carry out the experimentation. The different statistical features are computed, analyzed and classified. The accuracy rate of 91.16% for Sobel method, 96.80% for Otsu’s method, 94.92% for texture method and 97.55% for Prewitt method is obtained. The results are more promising and competitive which are validated by medical experts.

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Detection and Classification of Signage’s from Random Mobile Videos Using Local Binary Patterns

By Shivanand S. Gornale Ashvini K Babaleshwar Pravin L Yannawar

DOI:, Pub. Date: 8 Feb. 2018

The Traffic-Sign detection and recognition plays significant role in the design of autonomous driverless cars for navigation purpose as well as to assist a driver for alerting and educating him about the tracked signage on the road side. The main objective of this paper is to highlight an automatic process of detection of Region Of Interest (ROI) which marks or isolates signage’s from color video streams and performs classification of automatically detected signage’s based on support vector machine (SVM) classifiers trained over Local Binary Pattern (LBP) features. The training dataset was captured through 13 mega pixel mobile camera in different illumination and light conditions and due to randomness the data base complexity is very high. The robustness of the proposed system is measured on the bases its of capability of automatic detection and classification of ROI in a given video stream and backed with a comprehensive result analysis presented in this piece of work.

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Determination of Osteoarthritis Using Histogram of Oriented Gradients and Multiclass SVM

By Shivanand S. Gornale Pooja U. Patravali Kiran S. Marathe P. S. Hiremath

DOI:, Pub. Date: 8 Dec. 2017

Knee Osteoarthritis is most ordinary kind of joint inflammation, which often occurs in one or both the knee joints. Osteoarthritis is additionally called as 'wear and tear' process of joint that results in dynamic disintegration of articular cartilage. Cartilage is smooth substantial layer that ensures movement to occur effortlessly. In Osteoarthritis, the cartilage is inclined towards the destruction as it loses elasticity and becomes brittle.
Osteoarthritis is regularly investigated from radiographic evaluation after clinical examination. In any case, a visual evaluation made by the restorative physician depends on experience that varies subjectively and is profoundly reliant on their experience. Subsequently, in order to make diagnostic process more systematic and reliable, evolution of imaging based analysis for early recognition of Osteoarthritis is required. The objective of this study is to develop a machine vision approach for investigation of Knee Osteoarthritis using region based and active shape model. The computation involves histogram of oriented gradient (HOG) method. The processed HOG elements are computed using multiclass SVM for evaluating Osteoarthritis based on Kellgren and Lawrence (KL) grading system. The classification rate of 97.96% for Grade-0, 92.85% for Grade-1, 86.20% for Grade-2, 100% for Grade-3 & Grade-4 is obtained. The results are promising and competitive which are validated by the medical experts.

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