Abstract
One third of the world's population is thought to have been infected with mycobacterium tuberculosis (TB) with new infection occurring at a rate of about one per second. TB typically attacks the lungs. Indication of cavities in upper lobes of lungs shows the high infection. Traditionally, it has been detected manually by physicians. But the automatic technique proposed in this paper focuses on accurate detection of disease by computed tomography (CT) using computer-aided detection (CAD) system. The various steps of the detection process include the following: (i) image preprocessing, which is done by techniques such as resizing, masking, and Gaussian smoothening, (ii) image egmentation that is implemented by using mean-shift model and gradient vector flow (GVF) model, (iii) feature extraction that can be achieved by Gradient inverse coefficient of variation and circularity measure, and (iv) classification using Bayesian classifier. Experimental results show that its perfection of detecting cavities is very accurate in low false positive rate (FPR).
Highlights
Even though many effective methods have been taken to reduce the effect of TB, it is a third high rated disease causing death every year since just X-rays are used for detection process
This paper focuses on accurately detecting TB cavities from computed tomography (CT) images by computer-aided detection (CAD) system
For detecting cavities before handling the classification technique, the segmentation of the image takes place, in which initial contour is done before active contour
Summary
Even though many effective methods have been taken to reduce the effect of TB, it is a third high rated disease causing death every year since just X-rays are used for detection process. All commercial computer-aided detection systems use specific threshold values to determine whether an identified suspicious region is listed as positive findings, and the performance of these systems are frequently evaluated on the basis of the case-based sensitivity achieved at a given false-positive detection rate It is developed for diagnosis of various diseases and has become commonly used in routine diagnostic procedures such as, the diagnosis of breast cancer and lung cancer [4]. A CT scan takes these axial image, compiles them together, and recreates how the tissues and organs are located inside the body, aiding doctors to arrive at an accurate diagnosis By following this technique, intensity of infection would be accurately determined. In [16], a technique to detect TB has been proposed, based on binarization process to set edge lines of ribs, Gradient vector flow model for segmentation and K-means algorithm for classification. A survey of Automatic screening for TB in chest radiographs shows that TB screening is a challenging task and an open research problem [20]
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