An Adaptive K-means Clustering Algorithm for Breast Image Segmentation

Patel, Bhagwati Charan; Sinha, Gautam

doi:10.5120/1467-1982

Cited by 60 publications

(21 citation statements)

References 9 publications

(8 reference statements)

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“…K-means segmentation algorithm is an unsupervised machine learning method, which is characterized by high efficiency and simple implementation [41][42][43].…”

Section: K-means Segmentation Algorithmmentioning

confidence: 99%

Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China

Wang

Lin

et al. 2018

Remote Sensing

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Abstract:Although optical remote sensing can intuitively detect algal bloom, it is limited by the weather conditions. Synthetic aperture radar (SAR) is not affected by inadequate weather conditions. According to visual interpretation of SAR images and comparisons of quasi-synchronized optical images, the gathering areas of algal bloom present as "dark regions" on SAR images. It is shown that using SAR to monitor the water surface is workable. However, dark regions may also be caused by other factors, such as low wind speeds. This challenges with SAR monitoring of algal bloom on the water surface. In this study, an improved K-means algorithm, combined with multi-Otsu thresholding algorithm, was proposed to segment the dark regions. After feature analysis and extraction of Sentinel-1A images, an algal bloom recognition model with a support vector machine (SVM) was applied to discriminate the algal bloom dark regions from the low wind dark regions. According the experimental results, the overall accuracy achieved 74.00% in Taihu Lake. Additionally, this method was also validated in Chaohu Lake and Danjiangkou Reservoir. Therefore, it can be concluded that SAR can provide a new technical means for monitoring algal bloom of inland lakes, particularly when it is cloudy and unsuitable for optical remote sensing. To obtain more information about algal bloom, multi-band and multi-polarization SAR images can be considered for future.

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“…K-means segmentation algorithm is an unsupervised machine learning method, which is characterized by high efficiency and simple implementation [41][42][43].…”

Section: K-means Segmentation Algorithmmentioning

confidence: 99%

Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China

Wang

Lin

et al. 2018

Remote Sensing

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“…Thus far, several studies have applied computer-aided diagnosis (CAD) techniques to breast cancer detection; these techniques include the use of artificial neural networks [13][14][15], fuzzy logic [16,17], Bayesian networks [18,19], decision trees [20,21], and k-means clustering [22,23]. However, few researchers have implemented CAD methods with DOT to diagnose breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network for breast cancer diagnosis using diffuse optical tomography

Wang

Jiang

2019

Vis. Comput. Ind. Biomed. Art

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We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system, which is suitable for repeated measurements in mass screening. Sixty-three optical tomographic images were collected from women with dense breasts, and a dataset of 1260 2D gray scale images sliced from these 3D images was built. After image preprocessing and normalization, we tested the network on this dataset and obtained 0.80 specificity, 0.95 sensitivity, 90.2% accuracy, and 0.94 area under the receiver operating characteristic curve (AUC). Furthermore, a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset. The sensitivity, specificity, accuracy, and AUC of the classification on the augmented dataset were 0.88, 0.96, 93.3%, and 0.95, respectively.

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“…Salem [21] reported a similar technique to find white cells using a k-means algorithm. And Patel and Sinha [22] used a clustering algorithm based on adaptive k-means, diversifying the parameters to boost image segmentation's performance. In general, clustering is easy to implement, simple, and the results are easy to interpret.…”

Section: Introductionmentioning

confidence: 99%

A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis

et al. 2019

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Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammograms using the Dunn index as an optimization function, and the grey levels to represent each individual. The update of grey levels during the process results in the maximization of the Dunn's index function; the higher the index, the better the segmentation will be. The results showed a lower error rate using these meta-heuristics for segmentation compared to a well-adopted classical approach known as the Otsu method.

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An Adaptive K-means Clustering Algorithm for Breast Image Segmentation

Cited by 60 publications

References 9 publications

Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China

Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China

Convolutional neural network for breast cancer diagnosis using diffuse optical tomography

A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis

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