Lossless Compression – 3D Medical Images

Medical imaging methods like Magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission CT (SPECT) produce volumes of three‐dimensional (3D) images obtained from generating several slices in a single examination. This massive amount of medical data calls for new lossless compression methods for efficient storage and transmission.

Researchers have developed an object‐based hybrid lossless compression technique for three‐dimensional (3D) medical images. Their approach utilizes two phases:

  1. Determining the volume of interest (VOI) for a given 3D medical imagery using the selective bounding volume (SBV) method.
  2. Obtaining VOI encoded with a hybrid lossless algorithm using Lembel‐Ziv‐Welch Coding (LZW) followed by arithmetic coding (L to A).

Experimental results show that the proposed 3D medical image compression method is comparable with existing standard lossless encoding methods such as Huffman Coding, Run Length Coding, LZW, and Arithmetic Coding. The method obtains superior results overall.

During the decompression process, the processed VOI is fused with the non-object region (background) using non-object coordinate details intentionally sent in the compressed file. Object-based image compression can improvise certain compression algorithms, and leads get high fidelity on clinically significant portions. Some of the related works of such VOI-based compression methods are discussed below. Even though object-based coding is the most reasonable solution for image compression, it is a constraint in terms of compression efficiency.

Read more 

Related Content: Image Retrieval For Computer-Aided Diagnosis