Computer Assisted System for Features Determination of Lung Nodule From Chest X Ray Image

The Computer based / assisted system is proposed in this paper for feature extraction of lung nodule from the simple chest X-ray image. In recent years, the image processing mechanisms are widely used in several medical areas for early detection and in deciding treatment stages, where the time and cost factor is very important to discover the disease in the patient. According to WHO -among the cancer, lung cancer is one of the most common causes of death worldwide. Therefore, early detection using diagnostic tests promises to reduce mortality from lung cancer. At present, increased work load on interpretation of digital images (X-Ray and CT) by radiologist can be a potential source of error due to fatigue in detecting subtle lesion. In this work, the problem of developing a computer based system for the extraction of maximum statistical / mathematical features from the lung X-ray image is considered. Further, these properties can be used to classify lung nodule as benign or Malignant from the chest X-ray image directly

Chest X-ray Enhancement For The Proper Extraction Of Suspicious Lung Nodule

CAD (Computer Aided Diagnosis) system can detect lung cancer only when the quality of the image is excellent. Certain enhancement techniques are employed to improve the quality of X-ray image. By using enhancement technique, CAD systems will able to detect even a small lung nodule from the noisy blurred X-ray images. Average mean filter and Medianfilter is used to remove noise (Gaussian and salt & pepper noise)from the image. Contrast stretching, histogram equalization,negativity, log transform and power law transformare used to improve the intensity and contrast related problem.High boost filtering is used for sharpening the details in the image. For segmentation, modified thresholding algorithm and morphological operationare used. In the present study, images from the JSRT database and the images which are collected from nearby local hospitals are used to test the performance of the algorithm