Segmentation of Masses from Breast Ultrasound Images using Parametric Active Contour Algorithm

Jumaat, Abdul Kadir; Rahman, Wan Eny Zarina Wan Abdul; Ibrahim, Arsmah; Mahmud, Rozi

doi:10.1016/j.sbspro.2010.12.089

Cited by 30 publications

(16 citation statements)

References 5 publications

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“…The accuracy of segmentation results was 95.53%. Yan and Toshihiro [10] proposed segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. The proposed spatial FCM is more tolerant to noise than the conventional one.…”

Section: Literaturementioning

confidence: 99%

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Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

Minavathi¹,

Murali²,

Dinesh³

2012

IJCA

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This work presents a new approach for classifying masses in breast ultrasound images. Detection and classification of masses in ultrasound images still remains a challenge because most of the ultrasound images contain speckle noise and fuzzy boundaries. Ultrasound (US) is an important adjunct to mammography in breast cancer detection as it increases the rate of detection in dense breasts. Ultrasound also does dynamic analysis of moving structures in breast thus it is used to analyze the functional behavior of breast. In the proposed method, ultrasound images are preprocessed using Gaussian smoothing to remove additive noise and anisotropic diffusion filters to remove multiplicative noise (speckle noise). Active contour method has been used to extract a closed contour of filtered image which is the boundary of the spiculated mass. Spiculations which make breast mass unstructured or irregular are marked by measuring the angle of curvature of each pixel at the boundary of mass. To classify the breast mass as malignant or benign we have used: the structure of mass in accordance with spiculations, elliptical shape of the mass and acoustic shadowing feature which is an important functional feature. We have used receiver operating characteristic curve (ROC) to evaluate the performance. We have validated the proposed algorithm on 100 sub images(40 spiculated and 60 non spiculated) and results shows 92.7% of sensitivity with 0.88 Area Under Curve. Proposed techniques were compared and contrasted with the existing methods and result demonstrates that proposed algorithm has successfully detected and classified mass ROI candidates in breast ultrasound images.

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Section: Literaturementioning

confidence: 99%

“…This creates a piecewise constant image from which the segmentation boundaries can be easily obtained. Perona and Malik [10] first proposed anisotropic diffusion. They apply an inhomogeneous process that reduces the diffusivity at those locations which have a larger likelihood to be edges.…”

Section: Preprocessingmentioning

confidence: 99%

Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

Minavathi¹,

Murali²,

Dinesh³

2012

IJCA

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“…In this research used generalized histogram equalization, Markov random field, and Gibbs random field for enhancing the ultrasound images. Jumaat et al [11] proposed the segmentation of masses from breast ultrasound images using parametric active contour algorithm. In this research used the median filter and histogram stretching method for enhancing the ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

A Web-based Integrated Medical Information System for Cholangiocarcinoma Screening and Periductal Fibrosis Ultrasound Image Processing

Suapang

Krissana²,

Tosranon³

2017

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

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Cholangiocarcinoma is the most common in northeast Thailand and the second of most common primary liver cancer worldwide. As an online information system, it reduces the burden more clinical research and education are required and eliminates the restrictions of the traditional isolated PACS workstations. In addition, The periductal fibrosis ultrasound images can be applied for the cholangiocarcinoma screening system. However, the ultrasound images are lack of contrast and a lot of speckle noise. This study developed a web-based integrated clinical information system and periductal fibrosis ultrasound image processing for cholangiocarcinoma to enable rapid access to both digital images and associated text reports for audit, education and research. Emphasizing confidentiality of anonymised data and access control, the system provides a virtual collaboration platform integrating various clinical data sets for research and continuing education. What's more, the study presented image processing algorithm to improve the lack of contrast, reduce the speckle noise and preserving the edge, smooth region in periductal fibrosis ultrasound image. The system can potentially support remote consulting and statistical analysis of aggregated multimodality data. Furthermore, the performance of algorithm from the experimental results shown that increase the performance for enhancing the periductal fibrosis ultrasound image for cholangiocarcinoma screening system.

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“…To be noticed, the level set method is one of the segmentation processes produce better medical images for diagnosis purposes [12]. This is because this method can be segmenting the boundaries in which the way of defining gradient and its initial curves of level set is very easy to define [13].…”

Section: Introductionmentioning

confidence: 99%

Which Method Is Providing Correct Geometry of Pancreas in Filtering and Segmenting Ultrasound Images?

Mh¹,

Mf²,

Derus³

et al. 2017

Nucl Med Biomed Imaging

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Pancreas is a small organ approximately six inches long, located at upper abdomen and adjacent to the small intestine. It is located at the back and deep into the human body. Therefore, it difficult to obtain a pancreas image clearly using ultrasound machine. In order to solve this problem, MATLAB software was used to filter and segment the ultrasound pancreas images using various methods. The images were selected based on high quality images produced from five subjects using AplioMX device from Toshiba ultrasound machine with a 3.5MHz convex transducer.In this study, there were various technique of filter used including Kaun filter, Wiener filter, Frost filter and Anisotropic Diffusion filter in order to reduce spackle noise of ultrasound pancreas images. Then, the filtered images were measured using Mean Square Error (MSE), Power Signal to Noise Ratio (PSNR), Average Difference (AD), Normalized Cross-Correlation (NCC), Maximum Difference (MD), Structural Content (SC), and Normalized Absolute Error (NAE) formula to evaluate the best quality images before undergo the segmentation process. As the result, Wiener filter was selected. In the segmentation process, the active countor method and level set method were evaluated. Then, the area of binary image and error percentage were calculated. As a conclusion, it shows that the filtering process using Wiener filter and segmentation method using level sets method has been successfully done to produce the best geometry of pancreas image.

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Segmentation of Masses from Breast Ultrasound Images using Parametric Active Contour Algorithm

Cited by 30 publications

References 5 publications

Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

Classification of Mass in Breast Ultrasound Images using Image Processing Techniques

A Web-based Integrated Medical Information System for Cholangiocarcinoma Screening and Periductal Fibrosis Ultrasound Image Processing

Which Method Is Providing Correct Geometry of Pancreas in Filtering and Segmenting Ultrasound Images?

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