A Review of Threshold based Segmentation for Skin Cancer with Image Processing
T.D. Srividya1, V. Arulmozhi2

1T.D. Srividya, Tiruppur Kumaran College for Women, Bharathiar University, Mangalam Road, Tiruppur (Tamil Nadu), India.
2V. Arulmozhi, Tiruppur Kumaran College for Women, Bharathiar University, Mangalam Road, Tiruppur (Tamil Nadu), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 08 April 2019 | Manuscript Published on 28 April 2019 | PP: 225-228 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10530275C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Detecting skin cancer in premature stage is vital and decisive. Nowadays skin cancer is considered as the most hazardous forms of cancer in humans. Skin cancer are of various types such as Melanoma, Basal and Squamous cell carcinoma, amongst Melanoma is most erratic. The Malignant Melanoma is one of the dangerous in humans. Early diagnosis can be curable. In Medical Image Diagnosis Computer vision plays an important role which is proved by many existing systems. This paper explains the method for detection of melanoma using image processing tools. The Efficient tools supporting quantitative medical diagnosis are computer analysis and image processing. So the feature extraction phase is enormously dependent on the detected region which has the disease. So suitable segmentation algorithm is required which can effectively detect the skin melanoma pixels in the information image. In this work, we have discussed various techniques which are used in the segmentation procedure. This paper focuses on the method for the detecting Melanoma Skin Cancer by Segmentation. The input to the system is the Dermoscopic Image and then by applying novel image processing techniques. The pre-processing approaches employed in detecting various stages include collection of Dermoscopic Images, filtering the images by using Dull Razor filtering for removing hairs and air bubbles in the image, converting to gray scale, noise filtering, segmenting the images using threshold, hybrid threshold, iterative threshold, multilevel thresholding and Automatic Threshold.
Keywords: Skin Cancer, Segmentation, Thresholding, Melanoma Detection, Digital Image, Carcinoma.
Scope of the Article: Image analysis and Processing