A comparison of two methods for the segmentation of masses in the digital mammograms

The evolution of level set segmentation needs an appropriate initialization and controlling parameters, which requires manual intervention. A new spatial fuzzy level set algorithm is proposed in this paper to facilitate the automatic mammogram image segmentation. The initial segmentation of the mass is obtained by spatial fuzzy clustering that incorporates the spatial information and local intensity information to compute the weighted summed image. The maximum membership cluster extracted from the fuzzy clustering is used as an initial contour for the level set segmentation that is used to refine the mass boundary. The control parameters of level set algorithm also estimated from the results of fuzzy clustering. Experimental results show that the proposed method gives promising results to effectively segment the mass regions in the mammograms.

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“…Dubey et. al., [11] performed a performance comparison of level set and watershed segmentation methods on mass segmentation. Jun et.…”

Section: Introductionmentioning

confidence: 99%

A spatial fuzzy based level set method for mammogram mass segmentation

Anitha

Peter

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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“…[1] The author here compared two distinct semi automated methods named level set and marker controlled watershed methods which works to achieve accuracy and fast segmentation of tumor detected. [2] The author presented here an adaptive contrast enhancement method for breast skin-air interface detection which is responsible for combining use of method named adaptive histogram equalization on tiny portion or interested area that are containing operators like actual edge and edge detection. [3] The author provided an easy and simplest approach for detecting tissues containing cancers in mammogram.…”

Section: Related Workmentioning

confidence: 99%

Content Based Image Retrieval System for Mammogram Images Using BI_RADS Standard and SVM.

Kaur¹

2018

IJRASET

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The mammogram images are exposed to region of interest algorithm for extraction of infected area. The region of interest algorithm is fine-tuned using the combination of red, green and blue color matrices thresholds. The threshold limits are discussed in later section of the paper. The features including spot size, shape, brightness, contrast, density and Zernike moments are used to generate a vector set that is used to retrieve the images from the data base. The data base used for CBIR system for mammograms is MIAS data base consisting of different class of mammogram images. The data base contains images in PGM format and is converted to jpeg format using the matlab function class. The database images are categorized into different class as per the database instruction. Equal no. of images from each class are used to train a support vector machine classifier. Rests of the images are used to test the algorithm.

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“…Watershed is an extent in which the rain or ice or melting snow converges in a single point in an area of land. Watershed segmentation gets its name from the approach in which the algorithm segments regions into watershed line and volume of catchment basins [14]. Image is considered as a landscape with holes.…”

Section: Watershed Segmentationmentioning

confidence: 99%

Analysis of Tumor Characteristics based on MCA Decomposition and Watershed Segmentation

Ponraj.D¹,

M²,

Poongodi³

et al. 2012

IJCA

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An accurate and standardized 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, two different methods for mass detection are applied. First method uses morphological component analysis and multiple layer thresholding. Second method uses watershed segmentation. Features are extracted and the best one is found out for efficient identification of breast cancer.

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A comparison of two methods for the segmentation of masses in the digital mammograms

Cited by 35 publications

References 11 publications

A spatial fuzzy based level set method for mammogram mass segmentation

A spatial fuzzy based level set method for mammogram mass segmentation

Content Based Image Retrieval System for Mammogram Images Using BI_RADS Standard and SVM.

Analysis of Tumor Characteristics based on MCA Decomposition and Watershed Segmentation

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