Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

Hemalatha, R.; Vijaybaskar, V.; Thamizhvani, T. R.

doi:10.1155/2018/4976372

Cited by 14 publications

(6 citation statements)

References 16 publications

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“…14 In the previous work, active contour techniques were compared and used for segmentation of synovial region. 25 Different active contour methods were analyzed with the help of performance metrics. Lankton contour method was defined to be more efficient.…”

Section: Discussionmentioning

confidence: 99%

“…The initialization process in active contour depends on the energy forces. From our earlier work, 25 different active contour techniques like Caselles, Chan-Vese, Bernard, Chumming Li, and Lankton are compared using statistical evaluation and classification using support vector machine (SVM). With the results of the SVM classifier, Lankton is determined to be the most efficient technique for segmentation of synovial region from the image after localization of skin, bone and joint regions.…”

Section: Synovial Region Detectionmentioning

confidence: 99%

“…With the results of the SVM classifier, Lankton is determined to be the most efficient technique for segmentation of synovial region from the image after localization of skin, bone and joint regions. 14,25,26 Therefore, active contour is used for segmentation of the synovial region.…”

Section: Synovial Region Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Hemalatha

Vijaybaskar

Thamizhvani

2019

Proc Inst Mech Eng H

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The pace of population aging is growing faster worldwide. The quality of life of the aging population is mostly affected by rheumatic diseases. With the increasing rate of rheumatoid arthritis in the aging population, technological advances in the field of automatic image processing and analysis have paved way for automatic detection and diagnosis of arthritis based on how the grade of the synovial region is designed. The proposed method is based on spatial analysis using intensity-based approach to segment the skin border, thresholding and connectivity algorithm for bone region segmentation, hit-or-miss transform for bone line segmentation and distance measure with image profile to detect the joint region. After this process of localization, the synovial region is determined using the active contour technique. In arthritis condition, synovitis also occurs which is categorized into four different grades based on the fluid expansion in the synovial region. The different grades are defined and analyzed through deep learning. Convolutional neural network in a deep learning algorithm is used to diagnose the particular grade of synovitis to describe the nature of arthritis. With these results, a module to detect the nature of arthritis automatically is defined.

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Section: Discussionmentioning

confidence: 99%

Section: Synovial Region Detectionmentioning

confidence: 99%

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Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Hemalatha

Vijaybaskar

Thamizhvani

2019

Proc Inst Mech Eng H

Self Cite

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“…The approach defined a histogram-based representation of bounding coefficient describing the relative relation of edge regions in the image. An approach of medical image segmentation using active contour approach is outlined in [10]. The approach developed a segmentation method for the synovial region in an ultrasound image.…”

Section: Related Workmentioning

confidence: 99%

Position Invariant Spline Curve Based Image Retrieval Using Control Points

Pande¹,

Chetty²

2019

IJIES

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In this paper Cubic Bezier curve-based image retrieval system is proposed. This system evaluates similarity of each image in its database to a query image in terms of shape characteristics. Then, returns those images within a desired range of similarity. The proposed system determines nonlinear relationship between image's features for more accurate similarity comparison between query image and existing database images. Among existing approaches to shape feature analysis, statistical approach to extract shape features is adopted here. It works in form of control points of spline curves from both given query image and images of available database. These control points are further used to find out the Fourier Descriptors. Control points and Fourier descriptors are used for image retrieval in proposed system. With the vast number of images available on-line, quality CBIR systems are critical. The performance and results obtained by proposed system are compared to other CBIR systems. Comparison reveals proposed system performance is state of the art. In many parameters it outperforms other CBIR systems.

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“…Active contour is an example of a spatially continuous method. And in the segmentation of anatomical structures, active contour is found to be superior than classical approaches (Hemalatha et al, 2018). Graph-cut, machine learning and clustering are the spatially discrete approaches.…”

Section: Introductionmentioning

confidence: 99%

Composite fuzzy-wavelet-based active contour for medical image segmentation

Mewada

Patel

Chaudhari

et al. 2020

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Purpose In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images. Design/methodology/approach The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach. Findings The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%. Originality/value The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

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Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

Cited by 14 publications

References 16 publications

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Position Invariant Spline Curve Based Image Retrieval Using Control Points

Composite fuzzy-wavelet-based active contour for medical image segmentation

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