tailieunhanh - Multi-level Image Enhancement for Pulmonary Tuberculosis Analysis

The paper presents the task of auto-detecting the tiny nodules, which will help to get more information of pulmonary tuberculosis (TB). We apply two image processing technique into lung tissue information recognition. (1) A repetitive smoothing-sharpening technique is proposed and its impact is assessed to beneficially enhance X-ray lung images. (2) The ridge detection algorithm is going to diagnose indeterminate nodules correctly, allowing curative resection of early-stage malignant nodules and avoiding the morbidity and mortality of surgery for benign nodules. The proposed technique is tested on lung X-ray images. Results show that the proposed methodology has high potential to advantageously enhance the image contrast hence giving extra aid to radiologists to detect and classify TB | ISSN No. 2278-3083 Volume 1 September - October 2012 International Journal of Science and Applied Information Technology Available Online at http pdfs Multi-level Image Enhancement for Pulmonary Tuberculosis Analysis Chandrika Parvathi . and P. Bhaskar3 Department of Instrumentation Technology Gulbarga University P G. Centre Yeragera - 584 133. Raichur Karnataka India crgolds@ ABSTRACT The paper presents the task of auto-detecting the tiny nodules which will help to get more information of pulmonary tuberculosis TB . We apply two image processing technique into lung tissue information recognition. 1 A repetitive smoothing-sharpening technique is proposed and its impact is assessed to beneficially enhance X-ray lung images. 2 The ridge detection algorithm is going to diagnose indeterminate nodules correctly allowing curative resection of early-stage malignant nodules and avoiding the morbidity and mortality of surgery for benign nodules. The proposed technique is tested on lung X-ray images. Results show that the proposed methodology has high potential to advantageously enhance the image contrast hence giving extra aid to radiologists to detect and classify TB. Keywords X-ray Lung Image Enhancement Hybrid Image Enhancement Repetitive Image Enhancement Canny Edge Detection Laplacien Filtering Wavelet Transformation Tuberculosis Cavities 1. INTRODUCTION The aim of image processing and image segmentation in this paper is to auto-detecting tuberculosis cavities from the lung X-ray image 1-2 . Therefore earlier and more certain detection with more effective screening methods can be expected to improve cure rates. The paper presents that to detect tiny tuberculosis cavities from X-ray image which may present the characteristic of pulmonary tuberculosis and proposes an algorithm that incorporates newer imaging and diagnostic methods to facilitate the evaluation and management of removing the pulmonary tuberculosis .

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