An effective color image segmentation approach using neutrosophic adaptive mean shift clustering

Guo, Yanhui; Şengür, Abdulkadir; Akbulut, Yaman; Shipley, Abriel

doi:10.1016/j.measurement.2018.01.025

Cited by 49 publications

(20 citation statements)

References 23 publications

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“…The extension paper related to the mean-shift algorithm that proof the convergence step in the mean-shift algorithm is proposed by Ghassabeh (2013). In the medical field, the mean-shift algorithm has been applied to medical research (Vallabhaneni and Rajesh, 2017), (Aparajeeta et al, 2018), (Guo et al, 2018;Mure et al, 2015;Bai et al, 2013;Yang et al, 2013). The idea of mean-shift algorithm is to find pixel with similar characteristics of the density and save the distinct pixel value.…”

Section: Mean-shift Algorithmmentioning

confidence: 99%

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Haryanto¹,

Pratama²,

Suhartanto³

et al. 2020

Journal of Computer Science

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Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.

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Section: Mean-shift Algorithmmentioning

confidence: 99%

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Haryanto¹,

Pratama²,

Suhartanto³

et al. 2020

Journal of Computer Science

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show abstract

“…Meanwhile, it also has good performance in the field of denoising. NS theory in image segmentation processing [24][25][26][27][28][29][30][31] was mainly used for pre-processing images, converting images to the NS domain, and filtering out indeterminacy. However, this paper adopts the method of the saliency map with the NS, which is applied for further processing the saliency map, judging the target, and filtering out the indeterminacy.…”

Section: Introductionmentioning

confidence: 99%

“…The indeterminacy filter was then used again after the image c-mean clustering. In another paper [31], the indeterminacy filter was also applied to reduce the indeterminacy of an image through the indeterminacy values and the neighborhood features, and then the image was segmented using mean shift clustering, where the bandwidth was determined by the indeterminacy of the image.…”

Section: Introductionmentioning

confidence: 99%

A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

et al. 2020

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Due to more or less deviations in the imaging system, there will be noise in the image, which makes the image segmentation inaccurate. To divide a natural image into a more accurate binary image, the target and background of the image are effectively separated to achieve a more effective segmentation result. Therefore, this paper proposes an image segmentation algorithm combining a saliency map and neutrosophic set (NS) theory. First, to overcome the problem of weak edges in the image, we highlight the details and use the guided filter to filter the various channels of the natural image. Then, the initial saliency map is generated. After the weighted superposition of the initial saliency map, the local entropy map and the gray scale map, the final saliency map can be generated using the nonlinear function, and it can effectively highlight the foreground information of the image. Second, the saliency map is transformed to the NS domain and interpreted by three subsets: true (T), indeterminate (I), and false (F). According to NS theory, the indeterminacy is reduced, and the segmentation results are finally obtained by using the method of threshold. Various experiments were done and compared with other state-of-the-art approaches. These experiments demonstrate the effect of the proposed work, which is fast and effective for de-noising.

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“…It has been widely used in dealing with uncertain information [23]. Due to this, the NS theory has been successfully introduced into many applications, such as medical diagnosis [24], skeleton extraction [25], image segmentation [26][27][28][29][30][31], and object tracking [7][8][9]32]. SVNS (single valued neutrosophic set) [33] is a subclass of the neutrosophic set with a finite interval for practical usage.…”

Section: Introductionmentioning

confidence: 99%

“…The cosine similarity measure was also utilized in [26,31]. Furthermore, the neutrosophic set-based MeanShift and c-means clustering methods were proposed to earn a more robust segmentation result [27,28,30].…”

Section: Introductionmentioning

confidence: 99%

Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation

et al. 2019

Symmetry

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An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters.

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An effective color image segmentation approach using neutrosophic adaptive mean shift clustering

Cited by 49 publications

References 23 publications

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation

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