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Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Wang Dan, Li Zairan, Dey Nilanjan, Ashour Amira S., Moraru Luminita, Simon Sherratt R., Shi Fuqian

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

546--558

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8 s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes.

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey Nilanjan, Rajinikanth V., Shi Fuqian, Tavares João Manuel R.S., Moraru Luminita, Arvind Karthik K., Lin Hong, Kamalanand K., Emmanuel C.

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 3

843--856

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance- Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation.