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Segmentation and border identification of cells in images of peripheral blood smear slides
ACSC '07 Proceedings of the thirtieth Australasian conference on Computer science - Volume 62
Gray-scale edge detection for gastric tumor pathologic cell images by morphological analysis
Computers in Biology and Medicine
Advanced Data Mining Techniques
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Grassland species characterization for plant family discrimination by image processing
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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Cancer is the fourth leading cause of death among medically certified deaths in Malaysia. The most reliable diagnostic method to diagnose gastric adenocarcinoma is by inspecting the microscopic images of samples obtained through biopsy. These images are analyses by pathologist to identify the presence of cancer. However the process is time consuming and the interpretation varies with different pathologist. The application of image analysis techniques can assist pathologist towards a more efficient and faster diagnosis. Thus, this paper introduces an image analysis framework to automatically recognize and distinguished between normal gastric and gastric adenocarcinoma cells. The framework consist of the three phases of image analysis; preprocessing phase where the color tone issues are solved by component separation; processing phase which includes the thresholding and morphological techniques to segment the cells; post processing to identify the perimeter, area and roundness of the cells. This study shows that it is possible to automatically recognize and differentiate images with normal and abnormal cells.