Khairil Amir Sayuti

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.

Investigation of random walks knee cartilage segmentation model using inter-observer reproducibility: Data from the osteoarthritis initiative

BACKGROUND Existing knee cartilage segmentation methods have reported several technical drawbacks. In essence, graph cuts remains highly susceptible to image noise despite extended research interest; active shape model is often constraint by the selection of training data while shortest path have demonstrated shortcut problem in the presence of weak boundary, which is a common problem in medical images. OBJECTIVES The aims of this study is to investigate the capability of random walks as knee cartilage segmentation method. METHODS Experts would scribble on knee cartilage image to initialize random walks segmentation. Then, reproducibility of the method is assessed against manual segmentation by using Dice Similarity Index. The evaluation consists of normal cartilage and diseased cartilage sections which is divided into whole and single cartilage categories. RESULTS A total of 15 normal images and 10 osteoarthritic images were included. The results showed that random walks method has demonstrated high reproducibility in both normal cartilage (observer 1: 0.83±0.028 and observer 2: 0.82±0.026) and osteoarthritic cartilage (observer 1: 0.80±0.069 and observer 2: 0.83±0.029). Besides, results from both experts were found to be consistent with each other, suggesting the inter-observer variation is insignificant (Normal: P=0.21; Diseased: P=0.15). CONCLUSION The proposed segmentation model has overcame technical problems reported by existing semi-automated techniques and demonstrated highly reproducible and consistent results against manual segmentation method.

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.

Beating the odds: a rare case of supracardiac total anomalous pulmonary venous return (TAPVR) in an adult patient

Total anomalous pulmonary venous return (TAPVR) is a rare congenital heart defect, and patients are usually symptomatic at a very young age. Survival to adulthood without surgical correction is extremely rare. We report a 33-year-old woman with a heart murmur and a history of a successful pregnancy. Echocardiogram revealed a large atrial septal defect with suspicious pulmonary vein anomaly. Chest radiograph demonstrated classical ‘snowman’ configuration. Cardiac catheterisation was consistent with anomalous pulmonary venous drainage. Cardiac CT confirmed supracardiac TAPVR, whereby all the pulmonary veins drain into the anomalous vein and finally to the superior vena cava. She remained asymptomatic and underwent a successful surgical repair.

Flexible non cartilage seeds for osteoarthritic magnetic resoance image of knee: Data from the osteoarthritis initiative

Difficulties in analyzing diseased knee cartilage is attributed to large anatomical variation caused by osteoarthritic features. Using expert initialization as crucial source of information, interactive method is very useful in osteoarthritic cartilage segmentation. However, high level of expert intervention remains an issue for existing interactive segmentation model. Therefore, a flexible seeds labelling method has been proposed to automatically cover non-cartilage tissue. Compared to manual segmentation, the proposed method lowers the processing time by 48% in observer 1 and 30% in observer 2. Besides, the proposed method has demonstrated high Dices reproducibility of 0.80 for observer 1 and 0.82 for observer 2. In future, inter-observer reproducibility should be performed to analyze the progression of knee osteoarthritis.

Lipomatous Caecal Polyp Causing Ileocaecal Intussusception in an Elderly Lady: A Case Report and Literature Review

Intussusception is an uncommon cause of intestinal obstruction especially in adult. Adult intussusception accounts for only 5% of all cases of intussusceptions. We presented a case of a 63-yearold lady who presented with 1 week history of rightsided colicky abdominal pain which gradually became generalized, associated with vomiting out bile stained fluids. After series of investigations and worsening condition, emergency exploratory laparotomy was performed an(d unfortunately ileocolic intussusception was found. The patient underwent right hemicolectomy. Histopathological report confirmed lipomatous polyps arising from ileocaecal junction. Most recommendations of management are going towards resection of the involved segment without attempt of reduction as the causes of the intussusceptions in adults are usually due to pathological conditions within the involved segment.