An Automatic Model Combining Descriptors of Gray-Level Co-Occurrence Matrix and HMAX Model for Adaptive Detection of Liver Disease in CT Images
محورهای موضوعی : Communication
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)
کلید واژه: Support vector machine, Liver CT scan, gray-level Co-occurrence matrix, hierarchical visual HMAX model,
چکیده مقاله :
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.
