ICTACT Journal on Image and Video Processing

V. Saravana Kumar ¹, E. R. Naganathan ²

Affiliations
1 Centre for Bioinformatics, Pondicherry University, IN
2 Department of Computer Science and Engineering, Hindustan University, IN

Abstract

Hyperspectral image analysis is a complicated and challenging task due to the inherent nature of the image. The main aim of this work is to segment the object in hyperspectral scene using image processing technique. This paper address a novel approach entitled as Segmentation of hyperspectral image using JSEG based on unsupervised cluster methods. In the preprocessing part, single band is picked out from the hyperspectral image and then converts into false color image. The JSEG algorithm is segregate the false color image properly without manual parameter adjustment. The segmentation has carried in two major stages. To begin with, colors in the image are quantized to represent several classes which can be used to differentiate regions in the image. Besides, hit rate regions with cognate color regions merging algorithm is used. In region merging part, K-means, Fuzzy C-Means (FCM) and Fast K-Means weighted option (FWKM) algorithm are used to segregate the image in accordance with the color for each cluster and its neighborhoods. Experiment results of above clustering method could be analyzed in terms of mean, standard deviation, number of cluster, number of pixels, time taken, number of objects occur in the resultant image. FWKM algorithm results yields good performance than its counterparts.

Keywords

Cluster, Region Growing, Hit Ratio Region, Class-Map, Quantize.

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S. Anantha Sivaprakasam ¹, E. R. Naganathan ²

Affiliations
1 Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Science and Engineering, Hindustan University, IN

Abstract

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%.

Keywords

Cervical Cancer Cell, PAP Smear Test, Segmentation, Classification, Morphological And Statistical Edge Based Segmentation, Morphological and Statistical Region Based Segmentation.

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