Segmentation of Breast Ultrasound image using Regularized K-Means (ReKM) clustering

Samundeeswari, E. S.; Saranya, P.; Manavalan, R.

doi:10.1109/wispnet.2016.7566362

Cited by 22 publications

(8 citation statements)

References 9 publications

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“…The advent of low-cost ultrasound hardware has prompted alongside it the development of pathology-driven, clinically applicable image analysis software such as computer-aided detection and diagnosis (CADD) and image guided evaluation tools designed specifically for cancer care. Supplementing innovations in hardware by layering on disease-specific, image analysis tools provides an opportunity for improved cancer care in resource-limited settings by decreasing the cost and expertise required for accurate cancer diagnoses and staging [16] , [17] . Continued enhancement of image analysis algorithms has decreased false positive rates and unnecessary biopsies, particularly in breast cancer diagnostics [7] , [18] .…”

Section: Imaging Technologies For the Detection And Diagnosis Of Cancmentioning

confidence: 99%

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

Haney

Tandon

Divi

et al. 2017

IEEE J. Transl. Eng. Health Med.

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As the burden of non-communicable diseases such as cancer continues to rise in low- and middle-income countries (LMICs), it is essential to identify and invest in promising solutions for cancer control and treatment. Point-of-care technologies (POCTs) have played critical roles in curbing infectious disease epidemics in both high- and low-income settings, and their successes can serve as a model for transforming cancer care in LMICs, where access to traditional clinical resources is often limited. The versatility, cost-effectiveness, and simplicity of POCTs warrant attention for their potential to revolutionize cancer detection, diagnosis, and treatment. This paper reviews the landscape of affordable POCTs for cancer care in LMICs with a focus on imaging tools, in vitro diagnostics, and treatment technologies and aspires to encourage innovation and further investment in this space.

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Section: Imaging Technologies For the Detection And Diagnosis Of Cancmentioning

confidence: 99%

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

Haney

Tandon

Divi

et al. 2017

IEEE J. Transl. Eng. Health Med.

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“…A gray-level thresholding method was proposed to find the region of interests (ROIs) of tumors, and the area growing method was employed for tumor segmentation on ROIs [ 8 ]. A method based on k-means clustering [ 34 ] was reported. The classic k-means clustering was enhanced by Ant Colony Optimization (ACO) in initializing cluster centroid, and a regularization term was added to the k-means clustering function to increase the stability of the clustering method.…”

Section: Related Workmentioning

confidence: 99%

Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction

et al. 2022

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Medical image semantic segmentation is essential in computer-aided diagnosis systems. It can separate tissues and lesions in the image and provide valuable information to radiologists and doctors. The breast ultrasound (BUS) images have advantages: no radiation, low cost, portable, etc. However, there are two unfavorable characteristics: (1) the dataset size is often small due to the difficulty in obtaining the ground truths, and (2) BUS images are usually in poor quality. Trustworthy BUS image segmentation is urgent in breast cancer computer-aided diagnosis systems, especially for fully understanding the BUS images and segmenting the breast anatomy, which supports breast cancer risk assessment. The main challenge for this task is uncertainty in both pixels and channels of the BUS images. In this paper, we propose a Spatial and Channel-wise Fuzzy Uncertainty Reduction Network (SCFURNet) for BUS image semantic segmentation. The proposed architecture can reduce the uncertainty in the original segmentation frameworks. We apply the proposed method to four datasets: (1) a five-category BUS image dataset with 325 images, and (2) three BUS image datasets with only tumor category (1830 images in total). The proposed approach compares state-of-the-art methods such as U-Net with VGG-16, ResNet-50/ResNet-101, Deeplab, FCN-8s, PSPNet, U-Net with information extension, attention U-Net, and U-Net with the self-attention mechanism. It achieves 2.03%, 1.84%, and 2.88% improvements in the Jaccard index on three public BUS datasets, and 6.72% improvement in the tumor category and 4.32% improvement in the overall performance on the five-category dataset compared with that of the original U-shape network with ResNet-101 since it can handle the uncertainty effectively and efficiently.

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“…The tumor segmentation can be regarded as pixel-level binary classification task. Traditional classifiers such as SVM [11]- [13], adaboost [14] and K-means [15], [16] are used to learn the statistical characteristics of tumor regions. Daoud et al [17] proposed an accurate and automatic algorithm to segment breast ultrasound images by combining image boundary and region information.…”

Section: Related Workmentioning

confidence: 99%

“…Sadek et al [16] used normalized cuts approach to segment ultrasound images into regions of interest where they can possibly finds the lesion, and then K-means classifier is applied to decide finally the location of the lesion. Samundeeswari et al [15] incorporated the traditional K-Means algorithm with Ant Colony Optimization and Regularization parameter to segment the lesion portion with maximum boundary preservation.…”

Section: Related Workmentioning

confidence: 99%

Label-Distribution Learning-Embedded Active Contour Model for Breast Tumor Segmentation

Meng

et al. 2019

IEEE Access

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Tumor segmentation is the foundation of breast ultrasound image analysis. However, intensity inhomogeneity occurred in ultrasound images results in the ambiguous segmentation. In order to tackle the challenge, this paper proposed label-distribution learning embedded active contour model for the breast tumor segmentation. Considering that reasonable exploitation of label ambiguity may help to improve performance, a deep pixel-wise label distribution learning model is first proposed to learn an ambiguous label map. The learned map is independent on the intensity variation, which is robust to the intensity inhomogeneity. After that, a novel label distribution learning embedded active contour model is proposed. The new energy function is developed by introducing the new label distribution fitting energy into the active contour model framework. The proposed new fitting energy can enforce the label of pixels to be similar to the learned distribution map, which improves the robustness to the intensity inhomogeneity. To demonstrate the effectiveness of the proposed method, we conduct the experiment on the breast ultrasound images database which consists of 135 benign cases and 51 malignant cases. Our experimental results demonstrated that the proposed method outperforms the state of the art.

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Segmentation of Breast Ultrasound image using Regularized K-Means (ReKM) clustering

Cited by 22 publications

References 9 publications

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction

Label-Distribution Learning-Embedded Active Contour Model for Breast Tumor Segmentation

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