A Novel Parallel Swarm Approach for Cell Image Segmentation
Publish Year: 1387
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICMVIP05_101
تاریخ نمایه سازی: 29 اردیبهشت 1387
Abstract:
In this paper, we propose a novel parallel particle swarm optimization approach for segmentation of extremely noisy cell images. In this approach the roblem dependent knowledge about cell shape is taken into account. To this end, an elliptical model of cell contour is incorporated into this model which shows etter
detection of cell boundary. First by using Canny edge detector the gradient image is produced. Then a kernel based dynamic clustering algorithm is employed to specify the image points with high probability of belonging to each cell. Finally a parallel particle swarm optimization algorithm is used to adjust the parameters of the cell
contour model to find the best matching. The segmentation results on the benchmark images of noisy human
thyroid and small intestine cell images demonstrate that the proposed method is very successful in segmenting
images of elliptically shaped cells.
Keywords:
Parallel particle swarm optimization , Noisy image , Cell image segmentation , kernel based dynamic clustering , Elliptical cell contour
Authors
Ehsan Shahamatnia
Islamic Azad University of Qazvin, Young Researchers Club Member
Ramin Ayanzadeh
Islamic Azad University, Science and Research branch YRC Member
Soleyman Pasban
Islamic Azad University of Qazvin
Fariborz Mahmoudi
Islamic Azad University of Qazvin
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