An Automatic Model Combining Descriptors of Gray-Level Co-Occurrence Matrix and HMAX Model for Adaptive Detection of Liver Disease in CT Images
الموضوعات :
Sanaz Bagheri
1
(Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Somayeh Saraf Esmaili
2
(Department of Biomedical Engineering, Garmsar Branch, Islamic Azad University,
Garmsar, Iran)
الکلمات المفتاحية: Support vector machine, Liver CT scan, gray-level Co-occurrence matrix, hierarchical visual HMAX model,
ملخص المقالة :
Liver cancer emerges as a mass in the right upper of the abdomen with general symptoms such as jaundice and ‎weakness. In recent years, the liver cancer has been responsible for increasing the rate of deaths. Due to some discrepancies in the ‎analytical results of CT images and the disagreement among specialists about different parts of the liver, ‎accurate diagnosis of possible conditions requires skill, experience, and precision. In this paper, a new ‎integrative model based on image processing techniques and machine learning is provided, which is used for ‎segmentation of damages caused by the liver disease on CT images. The implementation process consists of three ‎steps: (1) using discrete wavelet transform to remove noise and separate the region of interest (ROI) in the image; (2) ‎creating the recognition pattern based on feature extraction by Gray-Level Co-occurrence matrix and ‎hierarchical visual HMAX model; reducing the feature dimensions is also optimized by principle ‎component analysis and support vector machine (SVM) classification, and finally (3) evaluating the algorithm performance by using K-fold method. The results of implementation were satisfactory both in performance evaluation and use of ‎features selection. The mean recognition accuracy on test images was 91.7%. The implementation was in the ‎presence of both descriptors irrespective of feature dimension ‎reduction; with unique HMAX model and feature ‎dimension reduction and application of both ‎descriptors and reduction of feature dimensions and their effect ‎on recognition were measured.‎
