AIRCC's International Journal of Computer Science and Information Technology

Ashraf A. Aly ¹, Safaai Bin Deris ², Nazar Zaki ³

Affiliations
1 Information Technology Department, Al khawarizmi College, AE
2 University Technology Malaysia, MY
3 College of Information Technology, UAE University, AE

Abstract

Sensitive and accurate cell tracking system is important to cell motility studies. Recently, researchers have developed several methods for detecting and tracking the living cells. To improve the living cells tracking systems performance and accuracy, we focused on developing a novel technique for image processing. The algorithm we propose presents novel image segmentation and tracking system technique to incorporate the advantages of both Topological Alignments and snakes for more accurate tracking approach. The results demonstrate that the proposed algorithm achieves accurate tracking for detecting and analyzing the mobility of the living cells. The RMSE between the manual and the computed displacement was less than 12% on average. Where the Active Contour method gave a velocity RMSE of less than 11%, improves to less than 8% by using the novel Algorithm. We have achieved better tracking and detecting for the cells, also the ability of the system to improve the low contrast, under and over segmentation which is the most cell tracking challenge problems and responsible for lacking accuracy in cell tracking techniques.

Keywords

Cell Tracking, Segmentation Enhancement, Active Contour, Topological Alignments.

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Ashraf A. Aly ¹, Safaai Bin Deris ¹, Nazar Zaki ²

Affiliations
1 Universiti Teknologi Malaysia, MY
2 College of Information Technology, UAE University, AE

Abstract

Evaluating the previous work is an important part of developing segmentation methods for the image analysis techniques. The aim of this paper is to give a review of digital image segmentation techniques. The problems of digital image segmentation represent great challenges for computer vision. The wide range of the problems of computer vision may make good use of image segmentation. This paper study and evaluate the different methods for segmentation techniques. We discuss the main tendency of each algorithm with their applications, advantages and disadvantages. This study is useful for determining the appropriate use of the image segmentation methods and for improving their accuracy and performance and also for the main objective, which designing new algorithms.

Keywords

Active Contour, Segmentation Enhancement, Topological Alignments, Boundary Detection, Image Segmentation, Inversion Technique.

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Al Mutaz M. Abdalla ¹, Safaai Dress ¹, Nazar Zaki ²

Affiliations
1 Universiti Teknologi Malaysia, MY
2 UAE University, AE

Abstract

When researchers are studying the detection of breast tumors, there has been great attention to the modern textural features analysis of breast tissues on mammograms. Detecting of masses in digital mammogram based on second order statistics has not been investigated in depth. During this study, the breast cancer detection was based on second order statistics. The extraction of the textural features of the segmented region of interest (ROI) is done by using gray level co-occurrence matrices (GLCM) which is extracted from four spatial orientations; horizontal, left diagonal, vertical and right diagonal corresponding to (0°, 45°, 90° and 135°) and two pixel distance for three different block size windows (8x8, 16x16 and 32x32) . The results show that the GLCM at 0°, 45°, 90° and 135° with a window size of 8X8 produces informative features to classify between masses and non-masses. Our method was able to achieve an accuracy of 91.67% sensitivity and 84.17% specificity which is comparable to what has been reported using the state-of-the-art Computer-Aided Detection system.

Keywords

Artificial Neural Network, Mammogram, Masses, GLCM.

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