A new approach for image segmentation using improved k-means and ROI saliency map

Sharma, Priyansh; Sharma, Mayank; Tomar, Ranjeet Singh

doi:10.1080/02522667.2017.1372138

Cited by 13 publications

(2 citation statements)

References 3 publications

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“…The K-means clustering algorithm is an iterative clustering analysis algorithm that is suitable for unsupervised learning dataset analysis and has strong adaptability; thus, it is widely used in image segmentation [25][26][27].…”

Section: K-means Clusteringmentioning

confidence: 99%

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Li,

Han,

et al. 2024

Sensors

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Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

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Section: K-means Clusteringmentioning

confidence: 99%

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Li,

Han,

et al. 2024

Sensors

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“…The authors express that simple rules of thumb are not adequate to distinguish benign lesions from malignant lesions on their dataset. Finally, in [15], an image segmentation approach based on improved Kmeans and ROI saliency map is proposed. In the study, brain and breast MR images are segmented.…”

Section: Introductionmentioning

confidence: 99%

Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice

Çetinel

Mutlu

Gül

2020

International Journal of Applied Mathematics Electronics and Computers

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Breast cancer is one of the most common cancer types especially met in women. The number of breast cancer patients increases every year. Thus, to detect breast cancer at its early stages gains importance. Breast region detection is the pioneering step of breast cancer diagnosis researches performed via image processing techniques. The performance of computer-aided breast cancer diagnosis systems can be improved by exactly determining the breast region of interest. In this study, the goal is to determine a region of interest for breast MR images, in which one or more lesion can appear. The achieved region includes two breasts and lymph nodes. The proposed region of interest detection system is fully automatic and it utilizes several image processing techniques. At first, the local adaptive thresholding technique is applied to the noise-filtered grey level breast magnetic resonance images taken with ethical permissions from Sakarya Education and Research Hospital. After adaptive thresholding, connected component analysis is performed to exclude extra structures around the breast region as thorax area. This analysis selects the largest area in the binary image which corresponds to a gyrate region including breast area and lymph nodes over the backbone. Then, the integral of horizontal projection is calculated to determine an optimum horizontal line that allows setting the region of interest apart. In the following step, sternum midpoint is detected to separate the right breast from the left one. Finally, a masking operation is applied to get corresponding right and left breast regions in the original MR image. To evaluate the performance of the proposed study, the results of automatic region of interest detection system are compared with the manual region of interest selection performed by an expert radiologist. Dice similarity coefficient and Jaccard coefficient are used as performance criteria. According to the results, the proposed system can detect region of interest for computer-aided breast cancer diagnosis researches, exactly.

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Using entropy to construct and evaluate player’s value and sustainable development model

Chang

2019

Journal of Information and Optimization Sciences

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A new approach for image segmentation using improved k-means and ROI saliency map

Cited by 13 publications

References 3 publications

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice

Using entropy to construct and evaluate player’s value and sustainable development model

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