Article

Segmentation of lungs from CT scan images for early diagnosis of lung cancer

01/2006;

ABSTRACT Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. The CAD (Computer Aided Diagnosis) of lung CT generally first segment the area of interest (lung) and then analyze the separately obtained area for nodule detection in order to diagnosis the disease. For normal lung, segmentation can be performed by making use of excellent contrast between air and surrounding tissues. However this approach fails when lung is affected by high density pathology. Dense pathologies are present in approximately a fifth of clinical scans, and for computer analysis such as detection and quantification of abnormal areas it is vital that the entire and perfectly lung part of the image is provided and no part, as present in the original image be eradicated. In this paper we have proposed a lung segmentation technique which accurately segment the lung parenchyma from lung CT Scan images. The algorithm was tested against the 25 datasets of different patients received from Ackron Univeristy, USA and AGA Khan Medical University, Karachi, Pakistan.

0 0
 · 
0 Bookmarks
 · 
156 Views
  • [show abstract] [hide abstract]
    ABSTRACT: In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) scanned images by using Genetic Algorithms (GA) and morphological techniques. First of all, GA has been used for automated segmentation of lungs. Region of interests (ROIs) have been extracted by using 8 directional searches slice by slice and then features extraction have been performed. Finally SVM have been used to classify ROI that contain nodule. The proposed system is capable to perform fully automatic segmentation and nodule detection from CT Scan Lungs images. The technique was tested against the 50 datasets of different patients received from Aga Khan Medical University, Pakistan and Lung Image Database Consortium (LIDC) dataset. Keywordscomputer aided diagnosis-mathematical morphology-segmentation-thresholding
    05/2010: pages 133-140;
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: Medical image is usually comprised of region of interest (ROI) and region of non interest (RONI). ROI is the region that contains the important information from diagnosis point of view so it must be stored without any distortion. We have proposed a digital watermarking technique which avoids the distortion of image in ROI by embedding the watermark information in RONI. The watermark is comprised of patient information, hospital logo and message authentication code, computed using hash function. Earlier BCH encryption of watermark is performed to ensure inaccessibility of embedded data to the adversaries.
    Int. J. Comput. Math. 01/2011; 88:265-280.
  • [show abstract] [hide abstract]
    ABSTRACT: The primary checking for our health at hospital needs to include a chest x-ray as routine diagnosis because it effectively illustrates the lung diseases especially tuberculosis or lung cancer which are asymptomatic earlier. It is a convenient and quick process with a low cost in comparison with other studies. This paper presents an investigation of the radiographs of lung from the chest x-ray using on medical knowledge and balanced histogram. Selected images of lungs are depicted by the use of an active contour (e.g. snake algorithm) to find two regions of lungs (left and right). Then, such two regions of lungs are represented for two histograms which are profiles of two lung patterns. Such two histograms are compared for normal and abnormal lungs using a method of center of gravity (COG) to demonstrate the difference of both lung radiographs. If two histograms are balance, then the result is a normal case. However, if they are not balance, then it is an abnormal case. For the experimental results, the overall accuracy is at approximately 95% which there are 100 samples of patients for testing their lung images. All samples are previously checked from the medical doctors.
    01/2010;

Full-text (4 Sources)

View
126 Downloads
Available from
Jan 13, 2013