Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering

Kumar, S. N.; Fred, A. Lenin; Varghese, P. Sebastin

doi:10.1007/s10278-018-0149-9

Cited by 36 publications

(19 citation statements)

References 32 publications

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“…e difference in ADC value and r FA value of the contralateral normal white matter area and peritumoral white matter area of the malignant tumor was obvious (P < 0.05), and there was no obvious difference in FA value of the two (P > 0.05). It was similar to the research results of Kumar et al (2015) [19]. erefore, it can be inferred that r ADC value, ADC value, and r FA value were useful in the diagnosis of benign and malignant tumors.…”

Section: Discussionsupporting

confidence: 90%

Adoption of Diffusion Tensor Imaging under Optimized Fuzzy C-Means Cluster Algorithm in Intracerebral Benign and Malignant Tumor Resection

Liu

2021

Scientific Programming

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The study aimed to analyze the application of diffusion tensor imaging (DTI) in the surgery of benign and malignant intracranial tumors through improved fuzzy C-means (FCM). First, a method of combining the maximum and minimum distances was proposed to improve the FCM algorithm. Then, the optimized FCM was applied to the diffusion tensor imaging (DTI) diagnosis. The patients were rolled into the benign tumor group and the malignant tumor group, and relevant parameters were compared. Finally, the postoperative total resection rate and disability rate of the DTI experimental group and the traditional control (Ctrl) group were evaluated. It was found that the segmentation accuracy of the optimized FCM algorithm was higher than traditional one and the obtained corpus callosum edge contour was clearer. In 63 patients with intracranial space, there were obvious differences in pairwise comparison of meningioma and glioma, metastatic tumor’s apparent diffusion coefficient (ADC) value, relative apparent diffusion coefficient (r ADC) value, and relative anisotropy fraction (r FA) P < 0.05 . In terms of the ADC, r ADC, and r FA values of tumor parenchymal area, those of benign tumors were larger than malignant tumors P < 0.05 . The ADC value (8.21 ± 1.87) and r FA value (1.36 ± 0.41) of the contralateral normal white matter area of malignant tumor were greater than the ADC value (7.23 ± 2.31) and r FA value (0.61 ± 0.24) of the peritumor white matter area, with statistically significant differences P < 0.05 . The total cut rates of the experimental group and the Ctrl were 87.5% and 54.84%, and the disability rates were 6.25% and 34.38%. In conclusion, the optimized FCM has high accuracy. The ADC, r ADC, and r FA values of DTI are important in the diagnosis of intracranial tumors. Besides, DTI can improve the survival rate in guiding intracranial tumor resection.

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Section: Discussionsupporting

confidence: 90%

Adoption of Diffusion Tensor Imaging under Optimized Fuzzy C-Means Cluster Algorithm in Intracerebral Benign and Malignant Tumor Resection

Liu

2021

Scientific Programming

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“…Besides breast segmentation for % PD calculation, this algorithm has been used for other purposes. A group of researchers used a variation of the FCM, where the influence of spatial neighbor pixels and similar super-pixels is incorporated in the model, for lesion segmentation on brain and breast MRI as also in mammograms [ 35 ]. The idea of modifying this algorithm was held by the fact of FCM being highly sensitive to noise because spatial information was not considered.…”

Section: Resultsmentioning

confidence: 99%

Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

Mendes

Matela

2021

J. Imaging

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Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice.

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“…Without any doubt, AI in breast imaging goes far beyond supervised machine learning. Further pivotal AI methods such as unsupervised learning and deep learning have already shown high promise for the future development of our field [44,49,[55][56][57]. The complexity and importance of this topic call for a dedicated publication summarizing the state of the art of AI in breast imaging [18,44,51].…”

Section: Artificial Intelligencementioning

confidence: 99%

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Dietzel

Clauser

Kapetas

et al. 2021

Rofo

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Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. Key Points: Citation Format

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Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering

Cited by 36 publications

References 32 publications

Adoption of Diffusion Tensor Imaging under Optimized Fuzzy C-Means Cluster Algorithm in Intracerebral Benign and Malignant Tumor Resection

Adoption of Diffusion Tensor Imaging under Optimized Fuzzy C-Means Cluster Algorithm in Intracerebral Benign and Malignant Tumor Resection

Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

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