Analysis of human tissue densities: A new approach to extract features from medical images

Abstract

Identification of diseases based on processing and analysis of medical images is of great importance for medical doctors to assist them in their decision making. In this work, a new feature extraction method based on human tissue density patterns, named Analysis of Human Tissue Densities (AHTD) is presented. The proposed method uses radiological densities of human tissues in Hounsfield Units to tackle the extraction of suitable features from medical images. This new method was compared against: the Gray Level Co-occurrence Matrix, Hu's moments, Statistical moments, Zernike's moments, Elliptic Fourier features, Tamura's features and the Statistical Co-occurrence Matrix. Four machine learning classifiers were applied to each feature extractor for two CT image datasets:, one to classify lung disease in CT images of the thorax and the other to classify stroke in CT images of the brain. The attributes were extracted from the lung images in 5.2 ms and obtained an accuracy of 99.01% for the detection and classification of lung diseases, while the attributes from the brain images were extracted in 3.8 ms and obtained an accuracy of 98.81% for the detection and classification of stroke. These results show that the proposed method can be used to classify diseases in medical images, and can be used in real-time applications due to its fast extraction time of suitable attributes.