Template Matching Advances and Applications in Image Analysis

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition

Template Matching Advances and Applications in Image Analysis

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition

Challenges in Brain Magnetic Resonance Image Segmentation

Over the past several decades, the application of magnetic resonance imaging (MRI) has been rapidly expanding in the areas of brain research studies and clinical diagnosis. One of the most important steps in brain MRI data preparation is the removal of unwanted brain regions, which is followed by segmentation of the brain into three main regions – white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) – or into subregions. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, analyzing brain changes, delineating pathological regions, and surgical planning and image-guided interventions. Brain segmentation allows clinicians and researchers to concentrate on a specific region in the brain in their analyses. However, brain segmentation is a difficult task due to high similarities and correlations of image intensity among brain regions. In this review, image segmentation algorithms used for dividing the brain into different sectors were discussed in detail. The potential for using the fuzzy c-means (FCM) unsupervised clustering algorithm and certain hybrid (combined) methods to segment brain MR images was demonstrated. Additionally, certain validation techniques that are required to demonstrate the performance of segmentation methods in terms of accuracy rates were described.