Work place: Electronics Research Institute, Cairo, Egypt
Research Interests: Computer systems and computational processes, Computer Vision, Pattern Recognition, Image Processing
Heba Ahmed Elnemr is an Associate Professor at Electronics Research Institute, Cairo-Egypt. She received her B. Sc. degree, M. Sc. degree and Ph.D. degree in Electronics and Communications Engineering from Faculty of Engineering, Cairo University, Egypt. She has supervised several masters and Ph. D. students in the field of image Processing. Her research interests include pattern recognition, signal processing, biometrics, computer vision and image processing.
DOI: https://doi.org/10.5815/ijisa.2018.08.05, Pub. Date: 8 Aug. 2018
Lung is a vital organ that plays a pivotal role in every second of our lives. Lungs may be affected by a number of diseases, including pulmonary edema and cancer. These diseases deemed life-sustained diseases, so they possess high preferences in detection, diagnosis, and possible treatments. In this paper, we presented a textural feature analysis framework that is capable of detecting lung abnormalities (edema or cancer) using Laws masks texture features. Laws masks are conventional texture feature extractor, and considered as one of the best methods for texture analysis in image processing. However, computing and extracting the texture features through various masks are very time consuming, whereas lung diseases demand rapid yet accurate diagnosis. Today, increased efficiency is being achieved through parallelism, and this trend is believed to continue in the future, with all computing devices likely to have many processors. Therefore, our objective is to investigate a multi-level parallel algorithm on Laws masks to describe structural variations of lung abnormalities. To our knowledge, there are no published researches that employed parallel strategies for lung abnormalities detection using Laws method. The proposed system has been experimented on real CT lung images. The results indicate that Laws texture features are capable of discriminating among normal, edema and cancerous lungs. Furthermore, applying parallel processing approaches improves significantly the overall system performance.[...] Read more.
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