A Chan-Vese model based on the Markov chain for unsupervised medical image segmentation

Huang, Quanwei; Zhou, Yuezhi; Tao, Linmi; Yu, Weikang; Zhang, Yaoxue; Huo, Li; He, Zuo-Xiang

doi:10.26599/tst.2020.9010042

Cited by 26 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some scholars are devoted to the study of image feature extraction [1,2]. Feature extraction can be used for image classification [3,4], image segmentation [4][5][6][7], target detection [8][9][10][11], attention mechanism of the visual system [11][12][13][14][15][16] and other research directions. Images have individual features and common features, which are adversarial and interdependent in image recognition.…”

Section: Introductionmentioning

confidence: 99%

A Mixed Method for Feature Extraction Based on Resonance Filtering

Zhang¹,

Lu²,

Ding³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Machine learning tasks such as image classification need to select the features that can describe the image well. The image has individual features and common features, and they are interdependent. If only the individual features of the image are emphasized, the neural network is prone to overfitting. If only the common features of images are emphasized, neural networks will not be able to adapt to diversified learning environments. In order to better integrate individual features and common features, based on skeleton and edge individual features extraction, this paper designed a mixed feature extraction method based on resonance filtering, named resonance layer. Resonance layer is in front of the neural network input layer, using K3M algorithm to extract image skeleton, using the Canny algorithm to extract image border, using resonance filtering to reconstruct training image by filtering image noise, through the common features of the images in the training set and efficient expression of individual characteristics to improve the efficiency of feature extraction of neural network, so as to improve the accuracy of neural network prediction. Taking the fully connected neural network and LeNet-5 neural networks for example, the experiment on handwritten digits database shows that the proposed mixed feature extraction method can improve the accuracy of training while filtering out part of image noise data.

show abstract

Section: Introductionmentioning

confidence: 99%

A Mixed Method for Feature Extraction Based on Resonance Filtering

Zhang¹,

Lu²,

Ding³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…However, the outbreak of COVID-19 has brought a series of difficulties for the continuous development of sports industry [7] , [8] , [9] , [10] . For example, in the COVID-19 environment, people’s demands to sport goods are often fluctuant with time: when the pandemic condition around is improving, people are very confident to gain better health and therefore, people’s attitude towards sport goods is positive; on the contrary, when the pandemic condition around is getting worse, people have no much interests in sports and as a consequence, sport goods are not welcome any more.…”

Section: Introductionmentioning

confidence: 99%

Time-aware sport goods sale prediction for healthcare with privacy-preservation

Zhou

Feng

2023

ISA Transactions

View full text Add to dashboard Cite

“…In the field of dental imaging, AI-assisted diagnosis is mostly limited to dental and periodontal diseases, but AI has recently been developed and applied towards diagnosing various specific features, including soft tissue calcification 14 and tissue tumors 15 . In addition, Chane-Vese model for unsupervised learning is used for medical image segmentation 16 .…”

Section: Introductionmentioning

confidence: 99%

Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography

Song

Jeong²,

Kim

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Artificial intelligence (AI) is limited to teeth and periodontal disease in the dental field, and is used for diagnosis assistance or data analysis, and there has been no research conducted in actual clinical situations. So, we created an environment similar to actual clinical practice and conducted research by selecting three of the soft tissue diseases (carotid artery calcification, lymph node calcification, and sialolith) that are difficult for general dentists to see. Therefore, in this study, the accuracy and reading time are evaluated using panoramic images and AI. A total of 20,000 panoramic images including three diseases were used to develop and train a fast R-CNN model. To compare the performance of the developed model, two oral and maxillofacial radiologists (OMRs) and two general dentists (GDs) read 352 images, excluding the panoramic images used in development for soft tissue calcification diagnosis. On the first visit, the observers read images without AI; on the second visit, the same observers used AI to read the same image. The diagnostic accuracy and specificity for soft tissue calcification of AI were high from 0.727 to 0.926 and from 0.171 to 1.000, whereas the sensitivity for lymph node calcification and sialolith were low at 0.250 and 0.188, respectively. The reading time of AI increased in the GD group (619 to 1049) and decreased in the OMR group (1347 to 1372). In addition, reading scores increased in both groups (GD from 11.4 to 39.8 and OMR from 3.4 to 10.8). Using AI, although the detection sensitivity of sialolith and lymph node calcification was lower than that of carotid artery calcification, the total reading time of the OMR specialists was reduced and the GDs reading accuracy was improved. The AI used in this study helped to improve the diagnostic accuracy of the GD group, who were not familiar with the soft tissue calcification diagnosis, but more data sets are needed to improve the detection performance of the two diseases with low sensitivity of AI.

show abstract

A Chan-Vese model based on the Markov chain for unsupervised medical image segmentation

Cited by 26 publications

References 17 publications

A Mixed Method for Feature Extraction Based on Resonance Filtering

A Mixed Method for Feature Extraction Based on Resonance Filtering

Time-aware sport goods sale prediction for healthcare with privacy-preservation

Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography

Contact Info

Product

Resources

About