SOFT TOOL DEVELOPEMENT FOR CHARACTERIZATION OF LUNG NODULE FROM CHEST X-RAY IMAGE

CAD (Computer Aided Diagnosis) is a concept established by taking into account equally the roles of physician and computer to comment on disease. With CAD system, the performance given by computer does not have to be comparable to or better than that by physician, but need to be complementary to that by physician. To reduce the false positive and false negative diagnosis in determining whether the tumor is malignant or benign, doctors are taking help of CAD system. CAD using image processing technique has become one of the major research subjects in medical imaging and diagnostic radiology. Radiologist uses the CAD system output as a ‘second opinion’ and make the final decision to conform the disease. In present paper, suspicious area is segmented from the chest X-ray image after doing pre-processing of the image. Characterization of lung nodule means describing distinctive essential features. By doing segmentation, the features are extracted from the segmented region. All features are calculated from their respective mathematical formulas. Calculated features values vary according to arrangement of pixels in the image. Further this information can be used as an input to the CAD system for determining whether the segmented suspicious tumor area is malignant or benign. The proposed system will not replace the doctor’s role in detection of cancer but it will help doctor to take correct decision in short time with accuracy. It will act as second opinion before conformation of cancer

THE PERFORMANCE OF VARIOUS THRESHOLDING ALGORITHMS FOR SEGMENTATION OF BIOMEDICAL IMAGE

ABSTRACT In biomedical image processing, segmentation is required for separating suspicious organ from the medical radiography. In segmentation techniques, thresholding is widely used because of its intuitive properties, simplicity of implementation and computational speed. Thresholding divided intensity of the image into two sub groups 0 or 255 for 8 bit image. Biomedical images contain complex anatomy which makes the segmentation task difficult. Various algorithms have been proposed to threshold the image. These algorithms take into consideration one or two properties of image for computing threshold. This paper contains performance comparison of various thresholding algorithms by applying on the chest radiograph (X-ray Image)

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

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

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