Advances in medical imaging technologies have given rise for effective diagnostic procedures. The acquisition promptness and resolution enhancements of imaging modalities have given physicians more information, less invasively about their patients. Active contours are used to segment, match and track images of an atomic structure by manipulating constraints derived from the image data together with prior knowledge about the location, size, and shape of these structures. The level set method is referred as a part of active contour family. The major disadvantages of level set method are initialization of controlling parameters and time complexity. The proposed method adopts Robust Spatial Kernel Fuzzy C-Means (RSKFCM) and Lattice Boltzmann Method (LBM) to overcome these drawbacks. RSKFCM is based on standard Fuzzy C-Means algorithm which uses Gaussian RBF kernel function as distance metric and incorporates spatial information. The LBM uses the energy function to determine and reduce the actual processing time which addresses the time complexity. The proposed system combines both RSKFCM and LBM to form a hybrid approach, and the system is tested on a large set of MRI brain images and the experimental results are found to be improved with respect to time complexity.

This research work introduces a method of using color thresholds to identify two-dimensional images in MATLAB using the RGB Color model to recognize the Color preferred by the user in the picture. Methodologies including image color detection convert a 3-D RGB Image into a Gray-scale Image, at that point subtract the two pictures to obtain a 2-D black-and-white picture, filtering the noise picture elements using a median filter, detecting with a connected component mark digital pictures in the connected area and utilize the bounding box and its properties to calculate the metric for every marking area. In addition, the shade of the picture element is identified by examining the RGB value of every picture element present in the picture. Color Detection algorithm is executed utilizing the MATLAB Picture handling Toolkit. The result of this implementation can be used in as a bit of security applications such as spy robots, object tracking, Color-based object isolation, and intrusion detection.

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Today's technological advances in medical imaging have given rise to efficient diagnostic procedures. Segmentation identifies and defines individual objects with various attributes such as size, shape, texture, spatial location, contrast, brightness, noise, and context. Deformable segmentation methods are Active contours, which are used to match and track images of an atomic structure by determining constraints derived from the image data. Level set method is an integral part of active contour family, considerable work towards level set methods has identified two main disadvantages i.e., initialization of controlling parameters and time complexity. In this paper, the methodology employed proposes an enhanced Variational level set methodology for Magnetic Resonance (MR) brain image segmentation with heterogeneous intensity. Core concept of IFCM is based on Intuitionistic fuzzy set. Both the values of membership and non membership values for the purpose of labelling are utilized together. As the result of experimentation reveals the efficiency of the recommended IFCM algorithm and Lattice Boltzmann Method (LBM) to overcome the drawbacks of Level Set methods by using the energy function to reduce the processing time which addresses the time complexity issue. The proposed system combines of both IFCM and LBM to form a novel approach. The system is tested on a large set of MRI brain images, extensive research and experiments were carried over on the standard dataset and the results are found to be improved in identification of tumor size detection with respect to time complexity.