Aboul Ella Hassanien (Abo)

n/a

This paper introduces a hybrid scheme that combines the advantages of fuzzy sets and rough sets in conjunction with statistical feature extraction techniques. An application of breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. The introduced scheme starts with fuzzy image processing as pre-processing techniques to enhance the contrast of the whole image; to extracts the region of interest and then to enhance the edges surrounding the region of interest. A subsequently extract features from the segmented regions of the interested regions using the gray-level co-occurrence matrix is presented. Rough sets approach for generation of all reducts that contains minimal number of attributes and rules is introduced. Finally, these rules can then be passed to a classifier for discrimination for different regions of interest to test whether they are cancer or non-cancer. To measure the similarity, a new rough set distance function is presented. The experimental results show that the hybrid scheme applied in this study perform well reaching over 98% in overall accuracy with minimal number of generated rules. (This paper was not presented at any IFAC meeting).

Abstract: This paper presents a Fuzzy C-Means based image segmentation approach that benefits from the Minkowski distance as the dissimilarity measure, denoted as FCM-M, instead of the traditional Euclidean distance, herein identified as FCM-E. The proposed approach was applied on Liver CT images, and a thorough comparison between both methods was carried out. FCM-M provided better accuracy when compared to the traditional FCM-E, with an area under the ROC curve of 85.44% and 47.96%, respectively. In terms of statistical significant analysis, a twofold benefit was obtained from using the proposed approach: the performance of the image segmentation procedure was maintained, or even slightly increased in some situations, while the CPU processing time was significantly decreased. The advantages inherent to the proposed FCM-M pave the way to a whole new chain of fully automatic segmentation methods.

In this paper, we investigate the eect of using an optimum
number of clusters with Fuzzy C-Means clustering, for Liver CT image
segmentation. The optimum number of clusters to be used was measured
using the average silhouette value. The evaluation was carried out using
the Jaccard index, in which we concluded that using the optimum number
of clusters may not necessarily lead to the best segmentation results.

Machine learning and computer vision techniques have applied for evaluating food quality as well as crops grading. In this paper, a new classification system has been proposed to classify infected/uninfected tomato fruits according to its external surface. The system is based on feature fusion method with color and texture features. Color moments, GLCM, and Wavelets energy and entropy have been used in the proposed system. Principle Component Analysis (PCA) technique has been used to reduce the feature vector obtained after fusion to avoid dimensionality problem and save time and cost. Support vector machine (SVM) was used to classify tomato images into 2 classes; infected/uninfected using Min-Max and Z-Score normalization methods. The dataset used in this research contains 177 tomato fruits each was captured from four faces (Top, Side1, Side2, and End). Using 70% of the total images for training phase and 30% for testing, our proposed system achieved accuracy 92%.