Medical Image Compression Utilizing The Serial Differences and Coding Techniques

Ghalib Ahmed Salman · Ahmed Ahmed · HAREER MOAIAD HUSSEN

Different medical devices for imaging used by centers and clinics produce an increasing number of sequential medical images. ‎Different imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Fluoroscopy ‎produce a set of series for the same patient. Within these images, most of the image parts are fixed against noticeable changes ‎in the remaining part. This consumes non-ignorable storage space. This paper proposes a near-lossless compression ‎technique that considers the fixed image parts to focus on changing parts for a higher compression ratio. In some applications, lossless compression techniques are highly preferable against preferring lossy techniques in some applications. In other applications, near-lossless compression techniques are preferable to lossless and lossy compression techniques, where lossy ones may ‎lose significant details, and the lossless ones produce less compression ratios than near-lossless ones. Previous works dealt with Fluoroscopy images as individual images or using ‎video compression techniques. This work tends to handle the whole series of ‎images as an integrated object. This paper considers subtracting successive ‎images to detect ROI areas producing zero overall values over similar ‎areas and non-zero ones within ROI ones. The double coding technique and near-lossless concept of compression increase the compression ratio. ‎Conducted experiments showed encouraging results benchmarking the other published ‎works in medical image compression.‎

Rotation Invariant Technique for Sign Language Recognition

Mohamed T. Dardoh Al-Obaidi · Ali M. Sahan · Ali S. Al-Itbi

Sign language recognition is an assistive technology that has garnered significant attention from researchers, particularly with respect to its potential benefits for individuals with hearing impairments. This paper proposes an effective technique for sign language recognition based on the Contourlet Transform (CT) and deep learning. The CT is employed in the pre-processing stage to reduce complexity and processing time, while deep learning is utilized to extract and classify sign language features. The proposed method was evaluated using two sign language databases: a direct feed database and an American sign language database. The experimental analysis demonstrated that the proposed method gives good results in processing time by more than 70% while maintaining high accuracy