An Optimized Approach for Prostate Image Segmentation Using K‐Means Clustering Algorithm with Elbow Method

Sammouda, Rachid; El-Zaart, Ali

doi:10.1155/2021/4553832

Cited by 57 publications

(23 citation statements)

References 28 publications

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“…In this study, K -means was used to improve the clustering color image segmentation algorithm and obtain relatively accurate initial parameters of the algorithm through the image HSI color space adaptive, which can significantly reduce the requirements on initial input parameters of the image. The method of computing the similarity of each pixel using multidimensional feature vector can greatly reduce the misclassification rate of each pixel in the image, thus reducing the number of iterations during the operation of the algorithm, and improving the image segmentation efficiency while maintaining low time complexity [ 25 , 26 ]. K -means improved clustering color image segmentation algorithm before and after processing abnormal uterine bleeding patient vaginal ultrasound image display.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Vaginal Ultrasound under Improved Clustering Algorithm Combined with Hysteroscopy in Abnormal Uterine Bleeding

Wang

Long

2022

Computational Intelligence and Neuroscience

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In order to explore the diagnostic value of the improved clustering algorithm of vaginal ultrasound combined with hysteroscopy in abnormal uterine bleeding (AUB), 128 patients diagnosed with AUB in the hospital were selected as the research objects. A K-means improved clustering color image segmentation algorithm was designed and applied to AUB vaginal ultrasound image processing. The running time, mean square error (MSE), and peak to signal noise ratio (PSNR) were calculated to evaluate the algorithm, and the sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio were used to evaluate the diagnostic accuracy of the detection method. In addition, combined with hysteroscopy, a comprehensive evaluation of the diagnostic value of abnormal uterine bleeding diseases was implemented. The results showed that compared with the traditional K-means clustering algorithm, the running time of the improved K-means clustering color image segmentation algorithm in the training set was significantly shortened, the MSE was significantly decreased, and the PSNR was significantly increased ( P < 0.05 ). The sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio (90.5%, 93.2%, 84.3, and 96.3%) of AUB diagnosis were significantly improved in the algorithm of vaginal ultrasound combined with hysteroscopy ( P < 0.05 ). In summary, the combination of vaginal ultrasound and hysteroscopy based on K-means improved clustering color image segmentation algorithm can significantly improve the clinical diagnostic accuracy of AUB patients.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Vaginal Ultrasound under Improved Clustering Algorithm Combined with Hysteroscopy in Abnormal Uterine Bleeding

Wang

Long

2022

Computational Intelligence and Neuroscience

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“…Also, considering both the Calinski-Harabasz index and the silhouette coefficient, k = 2 or 3 might be candidate clustering numbers. Finally, the optimal number (k = 3) of clusters was determined by the elbow method, a heuristic approach for determining the appropriate point for the local optimum (41,42), as shown in Figure 6.…”

Section: Internal Cluster Validationmentioning

confidence: 99%

Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data

et al. 2022

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Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k-means clustering through a cross-sectional study of sleep disturbance patient data. The data were obtained from an anonymous electronic survey in the Republic of Korea Army (ROKA) members from August 16, 2021, to September 20, 2021. The survey instrument consisted of six sections: demographics, medical history, military duty, sleep-related assessments (Pittsburgh sleep quality index (PSQI), Berlin questionnaire, and sleeping environment), diet/nutrition-related assessments [dietary habit survey questionnaire and nutrition quotient (NQ)], and gastrointestinal-related assessments [gastrointestinal symptom rating scale (GSRS) and Bristol stool scale]. Principal component analysis (PCA) and a deep autoencoder were used to extract features, which were then clustered using the k-means clustering method. The Calinski-Harabasz index, silhouette coefficient, and within-cluster sum of squares were used for internal cluster validation and the final PSQI, Berlin questionnaire, GSRS, and NQ scores were used for external cluster validation. One-way analysis of variance followed by the Tukey test and chi-squared test were used for between-cluster comparisons. Among 4,869 survey responders, 2,579 patients with sleep disturbances were obtained after filtering using a PSQI score of >5. When comparing clustering performance using raw data and extracted features by PCA and the deep autoencoder, the best feature extraction method for clustering was the deep autoencoder (16 nodes for the first and third hidden layers, and two nodes for the second hidden layer). Our model could cluster three different PI types because the optimal number of clusters was determined to be three via the elbow method. After external cluster validation, three PI types were differentiated by changes in sleep quality, dietary habits, and concomitant gastrointestinal symptoms. This model may be applied to the development of artificial intelligence-based clinical decision support systems through electronic medical records and clinical trial protocols for evaluating the effectiveness of TEAM treatment.

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“…habitats) was determined ( 26 ). This typically requires additional analysis such as the within cluster sum-of-squared or ‘Elbow plot’ method ( 54 ). This method plots the number of clusters (x-axis) against the sum of squared distance between each point and the centroid (y-axis).…”

Section: Habitat Imaging In Glioblastoma: Status and Potentialmentioning

confidence: 99%

Visualising spatial heterogeneity in glioblastoma using imaging habitats

et al. 2022

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Glioblastoma is a high-grade aggressive neoplasm characterised by significant intra-tumoral spatial heterogeneity. Personalising therapy for this tumour requires non-invasive tools to visualise its heterogeneity to monitor treatment response on a regional level. To date, efforts to characterise glioblastoma’s imaging features and heterogeneity have focussed on individual imaging biomarkers, or high-throughput radiomic approaches that consider a vast number of imaging variables across the tumour as a whole. Habitat imaging is a novel approach to cancer imaging that identifies tumour regions or ‘habitats’ based on shared imaging characteristics, usually defined using multiple imaging biomarkers. Habitat imaging reflects the evolution of imaging biomarkers and offers spatially preserved assessment of tumour physiological processes such perfusion and cellularity. This allows for regional assessment of treatment response to facilitate personalised therapy. In this review, we explore different methodologies to derive imaging habitats in glioblastoma, strategies to overcome its technical challenges, contrast experiences to other cancers, and describe potential clinical applications.

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An Optimized Approach for Prostate Image Segmentation Using K‐Means Clustering Algorithm with Elbow Method

Cited by 57 publications

References 28 publications

Diagnostic Value of Vaginal Ultrasound under Improved Clustering Algorithm Combined with Hysteroscopy in Abnormal Uterine Bleeding

Diagnostic Value of Vaginal Ultrasound under Improved Clustering Algorithm Combined with Hysteroscopy in Abnormal Uterine Bleeding

Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data

Visualising spatial heterogeneity in glioblastoma using imaging habitats

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