MyMedR

Displaying all 5 publications

Abstract:

Sort:

Random walker with improved weighting function for interactive medical image segmentation

Yin LK, Rajeswari M

Biomed Mater Eng, 2014;24(6):3333-41.
PMID: 25227043 DOI: 10.3233/BME-141156

To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the initialized seeds is estimated and integrated into the weighting function. To test the performance of the proposed weighting model, several medical images that mainly made up of 174-brain tumor images are experimented. These experiments establish that the proposed method produces better segmentation results than the original random walks.
Fulltext Predictors of Functional Outcome in Unstable Ankle Fractures Treated Surgically - A Prospective Cohort Study

Balaji G, Bhukya S, Nema S, Rajeswari M, Vellaipandi V

Malays Orthop J, 2021 Mar;15(1):85-92.
PMID: 33880153 DOI: 10.5704/MOJ.2103.013

Introduction: Unstable ankle injuries require anatomical reduction and stabilisation for optimal outcome. In spite of adequate care, a few patients have poor outcome. In this study, we assessed the risk factors that predict the clinical outcomes in surgically treated unstable ankle fractures.
Material and methods: This prospective cohort study was conducted on 68 patients who underwent surgical management for an unstable ankle injury. Demographic details, fracture type and associated medical comorbidities were recorded. Pre-operative radiographic assessment was done for all patients. At the end of one year follow-up, clinical (American Orthopaedic foot and ankle society-AOFAS and Olerud-Molander ankle - OMAS) scores and radiological parameters were assessed and analysed.
Results: Fracture dislocation (0.008), diabetes mellitus (0.017), level of alchohol consumption (0.008) and pre-operative talocrural angle (TCA) > 100° (0.03) were significant predictors of poor outcomes as per AOFAS. Fracture dislocation (0.029), diabetes mellitus (0.004), pre-operative TCA > 100° (0.009), female gender (0.001), age more than 60 years (0.002) and open injuries (0.034) had significantly poor outcome as per OMAS. Other parameters (smoking, hypertension, classification, syndesmotic injury, medial clear space and tibiofibular overlap) did not affect the outcome significantly.
Conclusion: Our study showed that poor outcome predictors in unstable ankle fractures are age >60 years, female gender, diabetes mellitus, alcohol consumption, fracture dislocation, open fractures and pre-op TCA >100°.
Profiling the features of pre-segmented healthy liver CT scans: towards fast detection of liver lesions in emergency scenario

Pasha MF, Hong KS, Rajeswari M

Annu Int Conf IEEE Eng Med Biol Soc, 2011;2011:5169-73.
PMID: 22255503 DOI: 10.1109/IEMBS.2011.6091280

Automating the detection of lesions in liver CT scans requires a high performance and robust solution. With CT-scan start to become the norm in emergency department, the need for a fast and efficient liver lesions detection method is arising. In this paper, we propose a fast and evolvable method to profile the features of pre-segmented healthy liver and use it to detect the presence of liver lesions in emergency scenario. Our preliminary experiment with the MICCAI 2007 grand challenge datasets shows promising results of a fast training time, ability to evolve the produced healthy liver profiles, and accurate detection of the liver lesions. Lastly, the future work directions are also presented.
Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure

Loo CK, Rajeswari M, Rao MV

IEEE Trans Neural Netw, 2004 Nov;15(6):1378-95.
PMID: 15565767

This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models.
Wavelet energy-guided level set-based active contour: a segmentation method to segment highly similar regions

Achuthan A, Rajeswari M, Ramachandram D, Aziz ME, Shuaib IL

Comput Biol Med, 2010 Jul;40(7):608-20.
PMID: 20541182 DOI: 10.1016/j.compbiomed.2010.04.005

This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection.