Spring-Charged Particles Model to Improved Shape Recovery:An Application for X-Ray Spinal Segmentation

Deformable models are widely used in medical image segmentation methods, to find not only single but also multiple objects within an image. They have the ability to follow the contours of an object of interest, define the boundary of ROI (Region Of Interest) and improve shape recovery. However, these methods still have limitations in cases of low image quality or clutter. This paper presents a new deformable model, the Spring-Charged Particles Model (SCPM). It simulates the movement of positively charged particles connected by springs, attracted towards the contour of objects of interest which is charged negatively, according to the gradient-magnitude image. Springs prevent the particles from moving away and keep the particles at appropriate distances without reducing their flexibility. SCPM was tested on simple shape images and on frontal X-ray images of scoliosis patients. Artificial noise was added to the simple images to examine the robustness of the method. Several configurations of springs and positively charged-particles were evaluated by determining the best spinal segmentation result. The performance of SCPM was compared to the Charged Fluid Model (CFM), Active Contours, and a convolutional neural network (CNN) with U-Net architecture to measure its ability for determining the curvature of the spinal column from frontal X-Ray images. The results show that SCPM is better at segmenting the spine and determining its curvature, as indicated by the highest Area Score value of 0.837, and the lowest standard deviation value of 0.028

Brain Tumor Classification in MRI Images Using En-CNN

Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset

A NEW APPROACH OF LEARNERS' ASSESSMENT USING BLOOM'S TAXONOMY-BASED SERIOUS GAME

This paper describes: a scenario, agent specification, mapping of knowledge domain, an implementation of Bloom's taxonomy-based serious game (BoTySeGa), and players' response against the game. The development of BoTySeGa is pursued to the availability of an alternative assessment tool for learning in elementary school. It considers aspects: game knowledge, subject matter of parallelogram for 5th grade elementary school learners, and cognitive domain of Bloom's taxonomy. BoTySeGa's level of challenge is structured accommodates cognitive domain of Bloom for elementary school learners (knowledge, comprehension, application). To make sure that all functions and features work well; we conducted user acceptance test against the game prototype. We also took players' response to BoTySeGa utilizing five-points Likert-type of questionnaire. The questions are distributed in 15 items. User acceptance testing involving 85 learners of 5th grade elementary school shows that BoTySeGa has fulfilled the learning assessment requirement. With the response score ranged from 5 to 75; it is found that the average score of players' response to the implementation of BoTySeGa in learning is 59.93. This response value falls within "Positive" category