محسن كشاورزمهر

Pathology is a branch of medical science that discusses the impact of diseases and injuries at the tissue and cellular as well as molecular levels. Pathology is also the study of the cause and effect of the disease from tissue samples on a microscope slide. eva‎luating a sample of tissue under a microscope is inherently subjective and can be controversial among pathologists. Therefore, regional tissue analysis of the target can be extremely useful in determining the correct diagnosis and prediction. Image segmentation is usually used to find the location of objects and borders (lines, curves, etc.) in the image. More precisely, image segmentation is a process in which each pixel is labeled so that pixels with the same tag have similar properties. In histopathological images, segmentation is an important step in the diagnosis process. Therefore, we first introduce an area-based segmentation algorithm based on histopathological imaging that performs an important part of the diagnosis stage without the need for preprocessing. In the next step, we use an SVM classifier to remove the artifact areas and the segmentation error. The proposed algorithm consists of two parts. The first step is to classify the classifier by the features extracted from the reference images. In the second step, we classify the areas using the erel algorithm and the svm classifier. The comparison results show that the proposed algorithm, in addition to having high accuracy (53% accuracy for JACC measurement, and 69% accuracy for DSI measurement), has a completely stable performance in different conditions and will be able to detect cancer cells with high accuracy and stability.

هيستوپاتولوژي , تعيين ناحيه

Histopathology , area determination

Mohsen Keshavarz mehr

Dr. Ahmad Ayatollahi