Segmentation and characterization of masses in breast ultrasound images using active contour

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

doi:10.1109/icsipa.2011.6144126

Cited by 17 publications

(16 citation statements)

References 12 publications

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“…The segmented part of region by snake(balloon) are compared with the ground truth that is mass areas traced by the radiologist. With accuracy of 95% the snake successfully segmented the masses [9].…”

Section: Related Workmentioning

confidence: 99%

Noise Reduction and Segmentation of Ultrasound Images using Active Contours

Harlapur¹

2016

BIJSESC

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Ultrasound Imaging or Ultrasonography is a medical procedure for diagnosis of abnormalities. Manual method of abnormality detection and segmentation is difficult because chances of faulty segmentation is possible due to large dataset and variation of accuracy. So, computerized automatic segmentation is required because automatic segmentation is effective and accurate for detection of abnormalities. This paper is presented with the Segmentation of fibroid from 2D Imagessuch as Ultrasound. Here Active Contour method is used to segment a fibroid from an Ultrasound Image. Fibroid is a noncancerous tumor that grows in female body and that causes infertility. Preprocessing is also be done before segmentation, as ultrasound image has poor image quality and contains the noise of type speckle. So median filter is applied to clear the noise.

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Section: Related Workmentioning

confidence: 99%

Noise Reduction and Segmentation of Ultrasound Images using Active Contours

Harlapur¹

2016

BIJSESC

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“…Some of the aforementioned segmentation methods require manual selection of an initial region of interest (ROI) to obtain a proper segmentation (Boukerroui et al 1998;Chang et al 2010;Jumaat et al 2011). In addition, as previously discussed, the intrinsic properties of ultrasound images are not compatible with most standard segmentation techniques or make the segmentation task too complex with respect to the results obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Snake active contours have been applied to BUS segmentation with good results. In Jumaat et al (2011), parametric active contour models such as gradient vector flow and balloon were used in BUS mass segmentation, after pre-processing with median filtering and histogram equalization. A segmentation refinement stage was designed, integrating curvature information or even empirical knowledge to improve the initial result.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

Rodrigues

Braz

Pereira

et al. 2015

Ultrasound in Medicine & Biology

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Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.

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“…On top of that, the extracted boundaries of breast abnormalities can be used in disease diagnosis by identifying its shape and size [2,3,4,5,6]. The edge-based models and region-based models are two categories of segmentation models.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Regularization Based Active Contour Model for Extraction of Shape Boundaries in Vector Valued Images

Jumaat¹,

Nithya²,

Kumar³

2022

IJETAE

Self Cite

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—Extraction of shape boundaries also known as image segmentation that tries to divide a digital image into several segments for further processing is an important step in digital image analysis. The segmented images are useful in many applications such as object detection, object classification, biometric authentication, and disease diagnosis. This task can be accomplished effectively using active contour model. The two types of active contour models are global segmentation and selective segmentation. A global segmentation model is a way for segmenting an image's entire objects. Global models, unfortunately, are unable to segment a single object’s shape boundary that must be extracted. A selective segmentation model, which seeks to segment selected object’s shape boundary in an image, can overcome this restriction. The recent selective segmentation model for vector valued images has a significant computational cost due to the requirement of solving the curvature term, that result from the usage of traditional regularization term in the formulation. Hence, we proposed a new selective segmentation model for vector valued image by replacing the traditional regularization term with Gaussian function which is easier and faster to solve. The proposed model was tested using MATLAB software in segmenting synthetic, natural and medical images. Numerical experiments shows that the proposed model was about 247 times faster than the existing model with a comparable accuracy.

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Segmentation and characterization of masses in breast ultrasound images using active contour

Cited by 17 publications

References 12 publications

Noise Reduction and Segmentation of Ultrasound Images using Active Contours

Noise Reduction and Segmentation of Ultrasound Images using Active Contours

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

Gaussian Regularization Based Active Contour Model for Extraction of Shape Boundaries in Vector Valued Images

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