An Intelligent Hybrid Segmentation Model Based on Fuzzy Logic, Discrete Wavelet Transform In Digital Imaging for Detection of Gastric Cancer

Abstract

Gastric cancer is the first rank of cancer in Iran. If the disease is detected in early stages, treatment is probability to be furthered and treatment costs will be reduced. Due to the complexity of pathologic images and the fundamental challenges in these images, such as the poor contrast between the cells, cell overlapping and the contradiction in tissue coloring, the process of diagnosing this type of disease is difficult and therefore needs a proper method to eliminate these Problems. In this research, a smart model is proposed to solve these problems. Then a fuzzy-based system, a discrete wavelet transform, The region's growth and the voting mechanism are used to identify the cells. Then, an advanced morphological method was presented for separating overlapping cells. Then the cell's feature were extracted and based on it, the cells are classified using the support vector machine algorithm (SVM) with the RBF kernel. The proposed algorithm was applied to a dataset of patients including 96 Microscopy Images from Baghiyatallah Hospital in Tehran. The proposed model was evaluated using the ROC curve analysis. The results were approved by expert pathologists and reveal accuracy of 92.12% in detection of normal and cancerous cells and 94.14% in detection of benign and malignant cells which are promising for early diagnosis of this type of cancer