Analysis of Tumor Characteristics based on MCA Decomposition and Watershed Segmentation

Ponraj.D, Narain; M, Evangelin Jenifer.; Poongodi, P.; J, Samuel Manoharan.

doi:10.5120/5677-7714

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The watershed approach showed better performance than dynamic programming boundary tracing method and the plane fitting and dynamic programming method. Ponraj et al [13] proofed that, for mass detection, watershed segmentation yields an accurate result compared to morphological component analysis method. They used extracted features from tumors to compare both techniques.…”

Section: Fig 1: Watershed Immersion Simulationmentioning

confidence: 99%

An Improved Approach for Breast Cancer Detection in Mammogram based on Watershed Segmentation

Hefnawy¹

2013

IJCA

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An accurate technique for breast tumor segmentation is a critical step for monitoring and quantifying breast cancer. The fully automated tumor segmentation in mammograms presents many challenges related to characteristics of an image. In this paper, a hybrid segmentation algorithm, which combines the watershed transform and level set techniques, is proposed. Since watershed segmentation is based on pixel density variation that is present in all mass tumors, it was fairly successful in locating tumors under all conditions. However it is very sensitive to small variations of the image magnitude and consequently the number of generated regions is undesirably large and the segmented boundaries are not smooth enough. Meanwhile Level set methods offer a powerful approach for the medical image segmentation since it can handle any of the cavities, concavities, convolution, splitting or merging. However, this method requires specifying initial curves and can only provide good results if these curves are placed near symmetrically with respect to the object boundary. In our proposed technique a watershed segmentation algorithm was developed to initially locate breast mass tumor candidates. In order to facilitate and improve the detection step, the segmentation results is treated as the initial localization of the desired contour, and used in the following level set method, which provides closed, smoothed and accurately localized contours or surfaces. Experimental results show the significant improvement of the final segmentation accuracy. General TermsImage Processing. KeywordsWatershed Segmentation; Breast Cancer Mammogram Detection. Image segmentation is a process that partitions an image into its constituent regions or objects. Effective segmentation of complex images is one of the most difficult tasks in image processing. Various image segmentation algorithms have been proposed to achieve efficient and accurate results. Among these algorithms, watershed segmentation is a particularly attractive method. The major idea of watershed segmentation is based on the concept of topographic representation of image intensity. Meanwhile, watershed segmentation also embodies other principal image segmentation methods including discontinuity detection, thresholding and region processing. Because of these factors, watershed segmentation displays more effectiveness and stableness than other segmentation algorithms [6]. INTRODUCTIONIn this paper, an algorithm belonging to the category of hybrid techniques is proposed, since it results from the integration of morphological watershed transform and level set method [7], and develops robust and flexible object segmentation approach. The output of the watershed detection function is used as rough approximation of the desired regions in the image, and guide for the initial location of the seed points used in the following level set method. Experimental results show that this hybrid method can solve the weakness of each method, and provide accurate, smoothed segmentation results. THE ...

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Section: Fig 1: Watershed Immersion Simulationmentioning

confidence: 99%