Cancerous nuclei detection on digitized pathological lung color images
Sammouda, Rachid . 2002
In this paper, we propose a methodology (in the form of a software package) for automatic extraction of the cancerous nuclei in lung pathological color images. We first segment the images using an unsupervised Hopfield artificial neural network classifier and we label the segmented image based on chromaticity features and histogram analysis of the RGB color space components of the raw image. Then, we fill the holes inside the extracted nuclei regions based on the maximum drawable circle algorithm. All corrected nuclei regions are then classified into normal and cancerous using diagnostic rules formulated with respect to the rules used by experimented pathologist. The proposed method provides quantitative results in diagnosing a lung pathological image set of 16 cases that are comparable to an expert’s diagnosis.
Presents contributions to improve a previously published approach for the segmentation of magnetic resonance images of the human brain, based on an unsupervised Hopfield neural network.
The paper presents a method for automatic segmentation of sputum cells color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network.