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

A new algorithm (AHTD) is proposed to extract image features based on human tissue densities in medical images.AHTD is used to extract features from lung and brain CT images.AHTD is compared against three traditional feature extraction methods.The influence of the extraction method on the classification accuracy was assessed using four machine learning techniques.The results confirm the superiority and suitability of AHTD for use with medical images. 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, Hus moments, Statistical moments, Zernikes moments, Elliptic Fourier features, Tamuras 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 5.2ms 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 3.8ms 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.

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