TB detection using modified Local Binary Pattern features

Abstract: This paper explores a computer-aided detection scheme to aid radiologists in making a higher percentage of correct diagnoses when analysing chest radiographs. The approach undertaken in the detection process is to use several proprietary image processing algorithms to adjust, segment and classify a radiograph. Firstly, a Difference of Gaussian (DoG) energy normalisation method is applied to the image. By doing this, the effect of differing equipment and calibrations is normalised. Thereafter, the lung area is detected using Active Shape Models (ASMs). Once identified, the lungs are analysed using Local Binary Patterns (LBPs). This technique is combined with a probability measure that makes use of the the locations of known abnormalities in the training dataset. The results of the segmentation when compared to ground truth masks achieves an overlap segmentation accuracy of 87,598±3,986%. The challenges faced during classification are also discussed