Faculty Publications
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Item A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment(Elsevier Ltd, 2017) Simu, S.; Lal, S.In this paper, a study and performance comparison of various evolutionary and non-evolutionary segmentation techniques on digital hand radiographs for bone age assessment is presented. The segmented hand bones are of vital importance in process of automated bone age assessment (ABAA). Bone age assessment is a technique of checking the skeletal development and detecting growth disorder in a person. However, it is very difficult to segment out the bone from the soft tissue. The problem arises from overlapping pixel intensities between bone region and soft tissue region and also between soft tissue region and background. Thus there is a requirement for a robust segmentation technique for hand bone segmentation. Taking this into consideration we make a comparison between non-evolutionary and evolutionary segmentation algorithms implemented on hand radiographs to recognize bone borders and shapes. The simulation and experimental results demonstrate that multiplicative intrinsic component optimization (MICO) algorithm provides better results as compared to other existing evolutionary and non-evolutionary algorithms. © 2016 Elsevier LtdItem Fully automatic segmentation of phalanges from hand radiographs for bone age assessment(Taylor and Francis Ltd., 2019) Simu, S.; Lal, S.; Fadte, K.; Harlapur, A.Segmentation of bones from hand radiograph is an important step in automated bone age assessment (ABAA) system. Main challenges in the segmentation of bones are the intensity inhomogeneity caused by the irregular distribution of X-rays and the overlapping pixel intensities between the bone and soft tissue. Hence, there is a need to develop a robust segmentation technique to tackle the problems associated with the hand radiographs. This paper proposes a fully automatic technique for segmentation of phalanges from left-hand radiograph for bone age assessment. The proposed technique is divided into five stages which are pre-processing, extraction of Phalangeal region of interest, edge preservation, segmentation of phalanges and post-processing. Quantitative and qualitative results of proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation methods. Qualitative results of proposed segmentation technique are also validated by different medical experts. The segmentation accuracy achieved by proposed segmentation technique is 94%. The proposed technique can be used for development of fully ABAA of a person for better accuracy. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.Item A framework for automated bone age assessment from digital hand radiographs(Springer, 2020) Simu, S.; Lal, S.Bone age assessment (BAA) is a method or technique that helps in predicting the age of a person whose age is unavailable and can also be used to find growth disorders if any. The automated bone age assessment system (ABAA) depends heavily on the efficiency of the feature extraction stage and the accuracy of a successive classification stage of the system. This paper has presented the implementation and analysis of feature extraction methods like Bag of features (BoF), Histogram of Oriented Gradients (HOG), and Texture Feature Analysis (TFA) methods on the segmented phalangeal region of interest (PROI) images and segmented radius-ulna region of interest (RUROI) images. Artificial Neural Networks (ANN) and Random Forest classifiers are used for evaluating classification problems. The experimental results obtained by BoF method for feature extraction along with Random Forest for classification have outperformed preceding techniques available in the literature. The mean error (ME) accomplished is 0.58 years and RMSE value of 0.77 years for PROI images and mean error of 0.53 years and RMSE of 0.72 years was achieved for RUROI images. Additionally results also proved that prior knowledge of gender of the person gives better results. The dataset contains radiographs of the left hand for an age range of 0-18 years. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.