SEGMENTATION AND CLASSIFICATION OF CERVICAL CYTOLOGY IMAGES USING MORPHOLOGICAL AND STATISTICAL OPERATIONS

• Main Authors: S Anantha Sivaprakasam, E R Naganathan
• Format: Article
• Language: English
• Published: ICT Academy of Tamil Nadu 2017-02-01
• Series: ICTACT Journal on Image and Video Processing
• Subjects: Cervical cancer cell; Pap Smear Test; segmentation; classification; morphological and statistical edge based segmentation; morphological and statistical region based segmentation
• Online Access: http://ictactjournals.in/ArticleDetails.aspx?id=2915

Cervical cancer that is a disease, in which malignant (cancer) cells form in the tissues of the cervix, is one of the fourth leading causes of cancer death in female community worldwide. The cervical cancer can be prevented and/or cured if it is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is called Papanicolaou test or Pap test which is used to detect the abnormality of the cell. Due to intricacy of the cell nature, automating of this procedure is still a herculean task for the pathologist. This paper addresses solution for the challenges in terms of a simple and novel method to segment and classify the cervical cell automatically. The primary step of this procedure is pre-processing in which de-nosing, de-correlation operation and segregation of colour components are carried out, Then, two new techniques called Morphological and Statistical Edge based segmentation and Morphological and Statistical Region Based segmentation Techniques- put forward in this paper, and that are applied on the each component of image to segment the nuclei from cervical image. Finally, all segmented colour components are combined together to make a final segmentation result. After extracting the nuclei, the morphological features are extracted from the nuclei. The performance of two techniques mentioned above outperformed than standard segmentation techniques. Besides, Morphological and Statistical Edge based segmentation is outperformed than Morphological and Statistical Region based Segmentation. Finally, the nuclei are classified based on the morphological value The segmentation accuracy is echoed in classification accuracy. The overall segmentation accuracy is 97%.