Oriented grouping-constrained spectral clustering for medical imaging segmentation
Original medical images are often inadequate for clinical diagnosis. Certain prior information
can be used as an important basis for disease diagnosis and prevention. In this study, an …
can be used as an important basis for disease diagnosis and prevention. In this study, an …
A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and fuzzy C-means clustering enhanced by genetic …
Purpose Lung cancer is the second most common type of cancer prevalent in men
worldwide. The early diagnosis of lung cancer can reduce cancer-related deaths …
worldwide. The early diagnosis of lung cancer can reduce cancer-related deaths …
Region growing with convolutional neural networks for biomedical image segmentation
In this paper we present a methodology that uses convolutional neural networks (CNNs) for
segmentation by iteratively growing predicted mask regions in each coordinate direction …
segmentation by iteratively growing predicted mask regions in each coordinate direction …
Segmentation of cancer nodules in lung using radial basis function network and fuzzy C mean clustering
Cancer in the lung is a vital cause of major death for women and men in oncology. Initial
detection of the cancer is supportive of a remedy to cure the disease completely. The …
detection of the cancer is supportive of a remedy to cure the disease completely. The …
Fully Automated Coronal and Sagittal Chest Segmentation using Colour Features and Fuzzy C-Means Clustering in CT Images
ZF Khan - Biomedical and Pharmacology Journal, 2019 - go.gale.com
In this article, a Combination of Fuzzy logic and color features based segmentation
approach for parenchyma of lung from the Coronal and Sagittal Chest CT images is …
approach for parenchyma of lung from the Coronal and Sagittal Chest CT images is …