Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together

Slantlet transform-based segmentation and α -shape theory-based 3D visualization and volume calculation methods for MRI brain tumour

Magnetic Resonance Imaging (MRI) being the foremost significant component of medical diagnosis which requires careful, efficient, precise and reliable image analyses for brain tumour detection, segmentation, visualisation and volume calculation. The inherently varying nature of tumour shapes, locations and image intensities make brain tumour detection greatly intricate. Certainly, having a perfect result of brain tumour detection and segmentation is advantageous. Despite several available methods, tumour detection and segmentation are far from being resolved. Meanwhile, the progress of 3D visualisation and volume calculation of brain tumour is very limited due to absence of ground truth. Thus, this study proposes four new methods, namely abnormal MRI slice detection, brain tumour segmentation based on Slantlet Transform (SLT), 3D visualization and volume calculation of brain tumour based on Alpha (α) shape theory. In addition, two new datasets along with ground truth are created to validate the shape and volume of the brain tumour. The methodology involves three main phases. In the first phase, it begins with the cerebral tissue extraction, followed by abnormal block detection and its fine-tuning mechanism, and ends with abnormal slice detection based on the detected abnormal blocks. The second phase involves brain tumour segmentation that covers three processes. The abnormal slice is first decomposed using the SLT, then its significant coefficients are selected using Donoho universal threshold. The resultant image is composed using inverse SLT to obtain the tumour region. Finally, in the third phase, four original ideas are proposed to visualise and calculate the volume of the tumour. The first idea involves the determination of an optimal α value using a new formula. The second idea is to merge all tumour points for all abnormal slices using the α value to form a set of tetrahedrons. The third idea is to select the most relevant tetrahedrons using the α value as the threshold. The fourth idea is to calculate the volume of the tumour based on the selected tetrahedrons. In order to evaluate the performance of the proposed methods, a series of experiments are conducted using three standard datasets which comprise of 4567 MRI slices of 35 patients. The methods are evaluated using standard practices and benchmarked against the best and up-to-date techniques. Based on the experiments, the proposed methods have produced very encouraging results with an accuracy rate of 96% for the abnormality slice detection along with sensitivity and specificity of 99% for brain tumour segmentation. A perfect result for the 3D visualisation and volume calculation of brain tumour is also attained. The admirable features of the results suggest that the proposed methods may constitute a basis for reliable MRI brain tumour diagnosis and treatments

Tumor brain detection through MR images: a review of literature

Today's modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by using different tools and techniques. However, this paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation. Lastly, the paper concludes with a concise discussion and provides a direction toward the upcoming trend of more advanced research studies on brain image segmentation and Tumor detection

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumo