Hong Seng Gan

Medical image visual appearance improvement using bihistogram Bezier curve contrast enhancement: data from the Osteoarthritis Initiative.

Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of “adequate contrast enhancement” to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the images maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fishers Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection.

Interactive knee cartilage extraction using efficient segmentation software: Data from the osteoarthritis initiative

In medical image segmentation, manual segmentation is considered both labor- and time-intensive while automated segmentation often fails to segment anatomically intricate structure accordingly. Interactive segmentation can tackle shortcomings reported by previous segmentation approaches through user intervention. To better reflect user intention, development of suitable editing functions is critical. In this paper, we propose an interactive knee cartilage extraction software that covers three important features: intuitiveness, speed, and convenience. The segmentation is performed using multi-label random walks algorithm. Our segmentation software is simple to use, intuitive to normal and osteoarthritic image segmentation and efficient using only two third of manual segmentations time. Future works will extend this software to three dimensional segmentation and quantitative analysis.

Interactive medical image segmentation with seed precomputation system: Data from the Osteoarthritis Initiative

Although interactive segmentation helps lower the degree of human intervention, further upgrade to increase the efficiency and intuitiveness of interactive segmentation method remains requisite. Invariably, newly acquired medical images are not segmented instantly by radiologists due to heavy work load. Therefore, improvement can be made by capitalizing on the time interval between image acquisition and image segmentation. We proposed to pre-generate non-cartilage seeds during this time interval so the labeling process can be simplified. This enhanced interactive segmentation decreases processing time required by a radiologist without compromising the quality of original segmentation quality. Future works should focus on developing automatic pre-generation of different types of cartilage labels.

Multilabel graph based approach for knee cartilage segmentation: Data from the osteoarthritis initiative

Knee osteoarthritis is the second most dreadful disease after cardiovascular diseases. Affected patients will not have any effective cure and face the risk of undergoing total knee replacement in chronic stage. Quantitative analysis enhances our understanding of the pathophysiology of osteoarthritis. Nonetheless, manual segmentation is notorious for time- and resource-intensive. Hence, we propose a multilabel, semiautomated segmentation method based on random walks to facilitate the segmentation process. Random walks method is robust to noise, allows multiple objects segmentation and achieves global minimum solution. Our experiment results indicated that random walks achieved greater efficiency than manual segmentation while preserved the quality of knee cartilage segmentation as measured by the Dices coefficient.

Analysis of parameters’ effects in semi-automated knee cartilage segmentation model: Data from the osteoarthritis initiative

Unlike automated segmentation, the accuracy of semi-automated segmentation is affected by pertinent parameters such as observer, type of methods and type of cartilage. In this paper, we investigated the effect of these parameters on segmentation results. Based on Dice similarity index obtained from fifteen normal and ten diseased magnetic resonance images, a parameter estimation model was constructed to study the impact of each parameter. Then, we conducted deviance test to verify the effect’s significance. Our result showed that implementation of the proposed segmentation model would introduce positive effect (+0.12) on reproducibility compared to conventional random walks model. Furthermore, we have found intriguing results indicating cartilage normality has diminished effect on reproducibility and tibial cartilage’s result could be influenced by external factors as well. Lastly, our findings highlighted on the necessity of refinement for semi-automated segmentation.