ICTACT Journal on Image and Video Processing

S. Athinarayanan ¹, M. V. Srinath ²

Affiliations
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN

Abstract

Cervical cancer is second topmost cancers among women but also, it was a curable one. Regular smear test can discover the sign of precancerous cell and treated the patient according to the result. However sometimes the detection errors can be occurred by smear thickness, cell overlapping or by un-wanted particles in the smear and cytotechnologists faulty diagnosis. Therefore the reason automatic cancer detection was developed. This was help to increase cancer cell mindfulness, diagnosis accuracy with low cost. This detection process consists of some techniques of the image preprocessing that is segmentation and effective texture feature extraction with SVM classification. Then the Final Classification Results of this proposed technique was compared to the previous classification techniques of KNN and ANN and the result would be very useful to cytotechnologists for their further analysis.

Keywords

Cancer, Cervical Cancer, Classification.

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S. Athinarayanan ¹, M. V. Srinath ², R. Kavitha ³

Affiliations
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN
3 Department of Computer Science and Engineering, PSN College of Engineering and Technology, IN

Abstract

The majority of the women of the world were affected by the disease of cervical cancer. As a result of this disease, their death rate was increase as hasty level. Hence so many number of research people was focused this notion as their research interest and also they have done so many number of solutions for finding this cancer by using some image processing technique and achieved a good results only in advanced and high cost techniques of LBC, biopsy or Colposcopy test Images. Therefore the reason, the authors have chosen this problem and also did not only to find whether the patient is affected by a cancer or not. In addition to the patient was affected by this cancer means and also to identify which severity stage of this disease the patient could be live. Then this work has done in based on the images of low cost pap smear screening test by using various image processing techniques with the help of Computerized Image Processing Software Interactive Data Language (IDL-Image Processing Language). Thus the final reports would be very useful to the pathologists for further analysis.

Keywords

Cancer, Cervical Cancer, Nuclei Segmentation, Feature Identification, Classification.

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S. Athinarayanan ¹, K. Navaz ¹, R. Kavitha ², S. Sameena ³

Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN

Abstract

Planning of invigorating representation is a troublesome and testing process because of the unpredictability of the images and absence of models of the life systems that thoroughly catches the reasonable expressions in each structure. Cervical malignant growth is one of the noteworthy reasons for death among different kinds of the diseases in women around the world. Genuine and auspicious determination can keep the life to some dimension. Therefore, we have proposed a computerized dependable framework for the analysis of the cervical malignancy utilizing surface highlights and machine learning calculation in Pap smear images, it is extremely advantageous to anticipate disease, likewise expands the dependability of the determination. Proposed framework is a multi-organize framework for cell nucleus extraction and disease finding. To begin with, clamor expulsion is performed in the preprocessing venture on the Pap smear images. Exterior highlights are separated from these demand free Pap smear images. Next period of the proposed framework is classification that depends on these separated highlights, SVM classification is utilized. Over 94% exactness is accomplished by the classification stage, demonstrated that the proposed calculation precision is great at recognizing the disease in the Pap smear images.

Keywords

Cervical Cancer, Feature Extraction, DGTF, Classification, Hybrid Kernel SVM.

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S. Athinarayanan ¹, K. Navaz ¹, R. Lakshmi Priya ², V. Ebenezer ³

Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, IN
3 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, IN

Abstract

In this paper, the iris prediction gives an incipient conception for Biometric benchmark process. Biometrics is the most secure and utilize-cordial benchmark implement. Iris perception technology contains pattern perception and optics method. It identifies an individual person by utilizing their individual physical characteristics. Iris perception system is very wide compared with other biometric systems. The main wholesomeness of iris perception system is its stability and uniqueness that results in a single enrolment for the lifetime. It provides increasingly varies than the fingerprint and the other biometrics systems, where the information cannot be stolen.

Keywords

Iris Prediction System, Pattern Analysis, Biometric Authentication, Human Eye.

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