Modified approaches on Lung Cancer Cell Extraction and Classification from Computerized Tomography Images
Antony Judice, K. Parimala Geetha, Rakhi Thampi · 2013
Lung Tumor D elineation is a critical aspect in radiotherapy treatment for cancer. It is usually performed with the anatomical images of a Computerized Tomography (CT) scan. An image processing techniques and Computer Aided Diagnosis systems has demonstrated to be an effectual system for an improvement of R adiologists, D iagnosis, especially in the case of Medical Image Processing. In this paper, we present an A utomatic C omputer - A ided D iagnosis system for an E arly D etection of L ung C ancer by an analyzing chest C omputed T omography (CT) images . One of the main difficulties limiting the segmentation of L ung T umors by CT images is the noise due to the patient's R espiratory movements. A detection of the L ung C ancer in its early stage can be helpful for medical treatment to limit the danger. Most traditional medical diagnosis systems are founded on huge quantity of training data and takes long processing time. So for reducing these proble ms the Hidden Markov Model is proposed. This method will increase the diagnosis confidence and also reduce the time utility. Histological data are used only for validation and comparison of segmenta tion tech nique. ( ANTONY JUDICE A, K. Parimala Geetha, R. Krishnan Thampi . Modified approaches on Lung Cancer Cell Extraction and Classification from Computerized Tomography Images . Life Sci J 2013;10(2): 162 1 - 1 62 6 ). (ISSN:1097 - 8135). http://www.lifesciencesite.com . 224