Evaluation of modified adaptive k-means segmentation algorithm

Debelee, Taye Girma; Schwenker, Friedhelm; Kebede, Samuel Rahimeto; Yohannes, Dereje

doi:10.1007/s41095-019-0151-2

Cited by 54 publications

(35 citation statements)

References 26 publications

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“…Most of the performance metrics encountered in the review include area under curve (AUC), sensitivity (Sn), specificity (Sp), accuracy (Acc), precision (P), recall (R), positive predictive values (PPV), Matthews correlation coefficient (MCC), geometric mean (G-Mean), which are usually successful in describing the classification performance [ 8 , 9 ]. Performance measures including Dice similarity coefficient (DSC) or Zijdenbos similarity index (ZSI) or F1-score, Hausdorff distance (H) and intersection over union (IoU) are the most effective metrics for measuring system’s segmentation performance [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…Region-based segmentation technique was in use in medical image analysis until the deep learning approach evolved in the field of computer vision [ 8 ]. However, Lee et al [ 7 ] in their survey paper indicated that the existence of deep learning in the research community has become a reason to use object recognition in an image.…”

Section: Deep Learning In Tumor Detection Segmentation and Classificationmentioning

confidence: 99%

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Deep Learning in Selected Cancers’ Image Analysis—A Survey

et al. 2020

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Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.

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Section: Methodsmentioning

confidence: 99%

Section: Deep Learning In Tumor Detection Segmentation and Classificationmentioning

confidence: 99%

Deep Learning in Selected Cancers’ Image Analysis—A Survey

et al. 2020

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“…Regionbased methods gather similar regions together according to the geometric information of the mesh. Well-known region-based works include K-means [16,17], clustering [18], hierarchical decomposition [13], primitive fitting [19], watersheds [20], random walks [21]. Boundary-based methods instead detect the geometric feature boundaries of the mesh which divide the mesh into different parts.…”

Section: General Mesh Segmentationmentioning

confidence: 99%

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Zhang

Song

et al. 2020

Graphical Models

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Automatic segmentation of 3D tooth models into individual teeth is an important step in orthodontic CAD systems. 3D tooth segmentation is a mesh instance segmentation task. Complex geometric features on the surface of 3D tooth models often lead to failure of tooth boundary detection, so it is difficult to achieve automatic and accurate segmentation by traditional mesh segmentation methods. We propose a novel solution to address this problem. We map a 3D tooth model isomorphically to a 2D harmonic parameter space and convert it into an image. This allows us to use a CNN to learn a highly robust image segmentation model to achieve automated and accurate segmentation of 3D tooth models. Finally, we map the image segmentation mask back to the 3D tooth model and refine the segmentation result using an improved Fuzzy-Clustering-and-Cuts algorithm. Our method has been incorporated into an orthodontic CAD system, and performs well in practice.

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“…In the practical application, to meet our needs, it is sometimes necessary to use multidimensional threshold segmentation methods or select multiple thresholds at the same time to achieve effective segmentation of the target grayscale image. As the number of grayscale image information dimensions or the number of selected thresholds increased, the computational complexity of the threshold segmentation algorithm increases rapidly [3]. It will take a long time, which to a certain extent limits the use of segmentation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Otsu’s Image Segmentation Algorithm with Memory‐Based Fruit Fly Optimization Algorithm

Chai

2021

Complexity

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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

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Evaluation of modified adaptive k-means segmentation algorithm

Cited by 54 publications

References 26 publications

Deep Learning in Selected Cancers’ Image Analysis—A Survey

Deep Learning in Selected Cancers’ Image Analysis—A Survey

Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space

Otsu’s Image Segmentation Algorithm with Memory‐Based Fruit Fly Optimization Algorithm

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