An implementation of novel genetic based clustering algorithm for color image segmentation

The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application

MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

Region-based color image segmentation is elementary steps in image processing and computer vision. Color image segmentation is a region growing approach in which RGB color image is divided into the different cluster based on their pixel properties. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced an improved algorithm MMFO (Modified Moth Flame Optimization) Algorithm for RGB color image Segmentation which is based on bio-inspired techniques for color image segmentation. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different no. of clusters is used during the segmentation process

Ex vivo assessment of basal cell carcinoma surgical margins in Mohs surgery by autofluorescence‐Raman spectroscopy: A pilot study

Background: Autofluorescence (AF)‐Raman spectroscopy is a technology that can detect tumour tissue in surgically excised skin specimens. The technique does not require tissue fixation, staining, labelling or sectioning, and provides quantitative diagnosis maps within 30 min. Objectives: To explore the clinical application of AF‐Raman microscopy to detect residual basal cell carcinoma (BCC) positive margins in ex vivo skin specimens excised during real‐time Mohs surgery. To investigate the ability to analyse skin specimens from different parts of the head‐and‐neck areas and detect nodular, infiltrative and superficial BCC. Methods: Fifty Mohs tissue layers (50 patients) were investigated: 27 split samples (two halves) and 23 full‐face samples. The AF‐Raman results were compared to frozen section histology, carried out intraoperatively by the Mohs surgeon and postoperatively by dermatopathologists. The latter was used as the standard of reference. Results: The AF‐Raman analysis was completed within the target time of 30 min and was able to detect all subtypes of BCC. For the split specimens, the AF‐Raman analysis covered 97% of the specimen surface area and detected eight out of nine BCC positive layers (similar to Mohs surgeons). For the full‐face specimens, poorer contact between tissue and cassette coverslip led to lower coverage of the specimen surface area (92%), decreasing the detection rate (four out of six positives for BCC). Conclusions: These preliminary results, in particular for the split specimens, demonstrate the feasibility of AF‐Raman microscopy for rapid assessment of Mohs layers for BCC presence. However, for full‐face specimens, further work is required to improve the contact between the tissue and the coverslip to increase sensitivity