Journal of Integrative Oncology

Department of Internal Medicine, Maulana Azad Medical College, New Delhi, India

Publications

• Perspective   
  Automated Segmentation of Nucleus, Cytoplasm, and Background of Pap-Smear Images Using a Trainable Pixel Level Classifier
  Author(s): Unnati Bhatia*
  
  Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save a life. Automated diagnosis and classification of cervical cancer from pap-smear images has become a necessity as it enables accurate, reliable and timely analysis of the condition?progress. Segmentation is a fundamental aspect of enabling successful automated pap-smear image analysis. In this paper, a potent algorithm for segmentation of the pap-smear image into the nucleus, cytoplasm, and background using pixel level information is proposed. Methods: First, a number of pixels from the nuclei, cytoplasm, and background are extracted from five hundred images. Second, the selected pixels are trained using noise reduction, edge detection, and texture filters to produce a pixel level classifier. Third, the pixel level classifier is validated us.. Read More»
  

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