Proc. SPIE. 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging
KEYWORDS: Surgery, Image segmentation, Image processing, Image resolution, Quantitative analysis, Medical imaging, Monte Carlo methods, Open source software, Medical image processing, Binary data, Mathematical morphology, 3D image processing
Binary morphology has innumerable applications in biomedical imaging, from segmentation to denoising. However, it suffers from inherently low precision. This is primarily because binary morphology is a binary technique, where each image voxel is all-or-nothing included or excluded. Many desirable structuring element shapes, especially circles or spheres, are poorly approximated on regular grids. Making things worse, common workflows involving multiple binary morphology iterations, such as opening or closing, compound this error. Also, small structuring elements often cannot be applied to 3D anisotropic image volumes. This work describes an extension to the theory of binary morphology, dubbed partial volume morphology or PVM, which allows the structuring element and/or image to hold fractional gray values to account for partial volumes. Partial volume morphology enables arbitrarily shaped structuring elements to be used, regardless of the underlying image resolution, with arbitrary precision. This technique also extends to 3D anisotropic volumes, allowing high precision morphological operations in anisotropic datasets heretofore impossible with binary morphology. This technique can be applied to a binary segmentation, where it provides subtle improvements and eliminates precision error in the intermediate steps of a multiple-operation workflow. Additionally, PVM is particularly suited for use on ‘soft’ segmentated data, where the partial volume contribution or probability at each point can be found. With segmentation and structuring elements both partial volume aware, partial volume morphology reaches its full potential as a high precision analytical tool. An open source reference implementation in Python, pvmpy, is provided.
Iterative reconstruction and other noise reduction methods have been employed in CT to improve image quality and to reduce radiation dose. The non-local means (NLM) filter emerges as a popular choice for image-based noise reduction in CT. However, the original NLM method cannot incorporate similar structures if they are in a rotational format, resulting in ineffective denoising in some locations of the image and non-uniform noise reduction across the image. We have developed a novel rotational-invariant image texture feature derived from the multiresolutional Stockwell-transform (ST), and applied it to CT image noise reduction so that similar structures can be identified and fully utilized even when they are in different orientations. We performed a computer simulation study in CT to demonstrate better efficiency in terms of utilizing redundant information in the image and more uniform noise reduction achieved by ST than by NLM.
Polycystic kidney disease (PKD) is a major cause of renal failure. Despite recent advances in understanding the biochemistry and genetics of PKD, the functional mechanisms underpinning the declines in renal function observed in the disorder are not well established. No studies investigating the distribution of cysts within polycystic kidneys exist. This work introduces regional cyst concentration as a new biomarker for evaluation of patients suffering from PKD. We derive a method to define central and peripheral regions of the kidney, approximating the anatomical division between cortex and medulla, and apply it to two cohorts of ten patients with early/mild or late/severe disease. Our results from the late/severe cohort show peripheral cyst concentration correlates with the current standard PKD biomarker, total kidney volume (TKV), signi cantly better than central cyst concentration (p < 0.05). We also find that cyst concentration was globally increased in the late/severe cohort (p << 0.01) compared to the early/mild cohort, for both central and peripheral regions. These findings show cysts in PKD are not distributed homogeneously throughout the renal tissues.