Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

Texture analysis (TA) of histological images has recently received attention as an automated method of characterizing liver fibrosis. The colored staining methods used to identify different tissue components reveal various patterns that contribute in different ways to the digital texture of the image. A histological digital image can be represented with various color spaces. The approximation processes of pixel values that are carried out while converting between different color spaces can affect image texture and subsequently could influence the performance of TA. Conventional TA is carried out on grey scale images, which are a luminance approximation to the original RGB (Red, Green, and Blue) space. Currently, grey scale is considered sufficient for characterization of fibrosis but this may not be the case for sophisticated assessment of fibrosis or when resolution conditions vary. This paper investigates the accuracy of TA results on three color spaces, conventional grey scale, RGB, and Hue-Saturation-Intensity (HSI), at different resolutions. The results demonstrate that RGB is the most accurate in texture classification of liver images, producing better results, most notably at low resolution. Furthermore, the green channel, which is dominated by collagen fiber deposition, appears to provide most of the features for characterizing fibrosis images. The HSI space demonstrated a high percentage error for the majority of texture methods at all resolutions, suggesting that this space is insufficient for fibrosis characterization. The grey scale space produced good results at high resolution; however, errors increased as resolution decreased

Non-Hodgkin lymphoma response evaluation with MRI texture classification

<p>Abstract</p> <p>Background</p> <p>To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.</p> <p>Methods</p> <p>A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests.</p> <p>Results</p> <p>NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.</p> <p>Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue.</p> <p>Conclusion</p> <p>Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.</p

MRI texture analysis of subchondral bone at the tibial plateau

OBJECTIVES: To determine the feasibility of MRI texture analysis as a method of quantifying subchondral bone architecture in knee osteoarthritis (OA).   METHODS: Asymptomatic subjects aged 20-30 (group 1, n = 10), symptomatic patients aged 40-50 (group 2, n = 10) and patients scheduled for knee replacement aged 55-85 (group 3, n = 10) underwent high spatial resolution T1-weighted coronal 3T knee MRI. Regions of interest were created in the medial (MT) and lateral (LT) tibial subchondral bone from which 20 texture parameters were calculated. T2 mapping of the tibial cartilage was performed in groups 1 and 2. Mean parameter values were compared between groups using ANOVA. Linear discriminant analysis (LDA) was used to evaluate the ability of texture analysis to classify subjects correctly.   RESULTS: Significant differences in 18/20 and 12/20 subchondral bone texture parameters were demonstrated between groups at the MT and LT respectively. There was no significant difference in mean MT or LT cartilage T2 values between group 1 and group 2. LDA demonstrated subject classification accuracy of 97 % (95 % CI 91-100 %).   CONCLUSION: MRI texture analysis of tibial subchondral bone may allow detection of alteration in subchondral bone architecture in OA. This has potential applications in understanding OA pathogenesis and assessing response to treatment.   KEY POINTS: • Improved techniques to monitor OA disease progression and treatment response are desirable • Subchondral bone (SB) may play significant role in the development of OA • MRI texture analysis is a method of quantifying changes in SB architecture • Pilot study showed that this technique is feasible and reliable • Significant differences in SB texture were demonstrated between individuals with/without OA