The purpose of medical image segmentation is to extract information such as volume, shape, motion of organs for detecting abnormalities from the medical image for improvement and fast diagnosis. In this paper, a segmentation algorithm has been implemented for foetus ultrasound image by particle swarm optimisation (PSO) K-means clustering algorithm with fuzzy filter. Impulsive noise inherent in ultrasound image has been removed using fuzzy filter. Then, PSO K-means clustering segmentation method is applied for partitioning foetus ultrasonic images into multiple segments, which applies an optimal suppression factor for the perfect clustering in the specified data set. Experimental results show that the proposed algorithm outperforms other segmentation algorithms like seeded region growing using PSO, fuzzy C-means and watershed in terms of segmentation accuracy for speckle noise added to foetus ultrasound medical images.
Visual cryptography (VC) is a secret sharing cryptographic technique, which permits visual information that is encrypted in such a way that the decrypted information shows the visual image. Security is the common problem that arises in the general VC technique. To secure the visual information, an effective method is proposed using a developed sailfish invasive weed optimization (SIWO) algorithm. At first, the grayscale image is shared with multiple parties, and here the shared images are encrypted and decrypted to reconstruct the original image back. The key generation is done using the developed SIWO-based data sharing approach. Here, the developed approach offered the peak signal-to-noise ratiof 34.870 and 33.711 dB for Lena and Cameraman images, respectively, and it attains better performance in terms of secret image sharing.
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