Diagnosis of Breast Cancer by Optical Image Analysis

Attia, Salim J.; Blackledge, Jonathan; Abood, Ziad M.; Agool, Ibrahim R.

doi:10.1049/ic.2012.0198

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…This step is to check each individual pixel to see whether it belongs to an object of interest or not.The color of the images is exploited, starting by the segmentation of cells based on computing the average value of the basic (R; G; B) color bands for the intercellular regions. The maximum and minimum limited values of these regions are computed for each color.We determine the average value using equation (11) Then we use this value for all color components according to suitable condition with equation: ( 11)…”

Section: Resultsmentioning

confidence: 99%

Detection of malignant Cases by Segmentation of Cells in Medical Images and Applying Fuzzy Logic Technique

Attia

2014

UTJsci

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The process of detection and segmentation of cells is considered in digital optical images of human breast tissue as important base to diagnose the diseases. The major features of malignancy are related with the cells. It is therefore essential to operate a segmentation of the image, to isolate the cells from the rest of image, i. e., from other tissue components, and from some other undesirable elements of images. The recognition process includes a segmentation algorithm based on an adaptive imaging threshold procedure that is sensitive to local ranges in pixel intensity (minimum-maximum values). The statistical features are extracted from the images of cells like median, mode, mean and standard deviation. Then the fuzzy logic method is applied to detect breast cancer

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Section: Resultsmentioning

confidence: 99%