International Journal of Advanced Communication Technology (IJACT)

Efficient Hexagonal Gridding Method for DNA Microarray Image

2024-05-15T07:23:18+00:00

In genetics, a deoxyribonucleic acid (DNA) microarray is a useful instrument that is frequently used to track thousands of genes' expression levels simultaneously. For DNA microarray, gene expression is done through microarray spot gridding, segmentation and intensity extraction. The gridding processes identify each microarray spot location and supply their coordinates for the spot. Many microarray technologies arrange their microarray spots in in rectangular fashion. However, a hexagonal grid arrangement is also being used to increase the density of the spot per unit area. While gridding microarray arranged in rectangular fashion is straightforward, gridding microarray arranged in hexagonal fashion posed its own challenge due to the irregular structure of the spot location. To solve this problem, this paper discusses two proposed methods to perform gridding for microarrays arranged in a hexagonal fashion, namely alternate-column and diamond-shape methods. These methods have been evaluated in nine different benchmark images that represent best, typical and worst-case images. Based on our experiments, the proposed diamond-shape and alternate-column methods achieved an average accuracy of 93.6% and 92.1%, respectively. In terms of computational load, the diamond shape consumes three times less computational loads as compared to the alternate-column.

A Comparative Study of Binary and Fibonacci Decomposition Watermarking for Medical Images

2024-11-13T08:42:02+00:00

This study addresses the critical challenge of embedding additional information into medical images, specifically focusing on the trade-off between watermark capacity and visual quality. The importance of this challenge lies in maintaining the diagnostic value of medical images while securely embedding auxiliary data such as patient identifiers or copyright information. The study conducts a comparative analysis of two watermarking schemes: binary decomposition and Fibonacci decomposition. The binary and Fibonacci decompositions were specifically applied by utilizing modified binary watermarks and leveraging the specific domain properties of the host medical images to minimize disruptions during the embedding process. The evaluation was performed on a dataset of brain magnetic resonance imaging (MRI) images. The watermark capacity was varied to assess its impact on visual quality, which was quantified using Peak Signal-to-Noise Ratio (PSNR). The results demonstrated that the Fibonacci decomposition method achieved a higher watermark capacity of up to 3.5 bpp while maintaining high visual quality, with an average PSNR value of 76.5 dB. These results indicate that the Fibonacci decomposition approach offers significant advantages in achieving a balance between high capacity and minimal image distortion, making it a promising solution for medical image watermarking applications.