What is the atlas in image segmentation?
Atlas-based segmentation exploits knowledge from previously labeled training images to segment the target image. In this paper, we focus on multi-atlas segmentation methods that map all labeled images onto the target image, which helps to reduce segmentation errors [6,8,11].
What is an atlas in image processing?
Abstract. Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels).
What is atlas-based auto segmentation?
The atlas-based auto-segmentation (ABS) software is used to automatically contour target tumors and normal tissues on the computed tomography (CT) images of a new patient using predefined atlases and a non-rigid registration technique [4].
What is EDGE-based segmentation in image processing?
Edge-based segmentation relies on edges found in an image using various edge detection operators. These edges mark image locations of discontinuity in gray levels, color, texture, etc. When we move from one region to another, the gray level may change.
What is atlas based registration?
Registration with an atlas is a method to recognize and label structures that is used especially for the analysis of medical images. Its application to images containing many objects, such as images of the human abdomen, may prove to be very challenging.
What is a medical atlas?
Atlas: The first vertebra in the neck. It supports the head at the base of the skull. Also known as the first cervical vertebra.
What is auto segmentation?
Auto Segmentation enables real-time explorations across the entire user base, in seconds. Explore across multiple dimensions, identify users across platforms, and reach out to the identified audience using rich push notifications, in-app messages, SMS, email, or integration with marketing automation platforms.
What is segmentation in radiotherapy?
INTRODUCTION. Segmentation of medical images aims to locate anatomic structures and delineate their boundaries on a digital source. In radiotherapy (RT), imaging is a necessary part of the treatment routine since it is used to identify the treatment target and the normal structures to be avoided during irradiation.
What are the advantages and disadvantages of edge based segmentation?
Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Disadvantages of edge based segmentation: Segmentation separates one or more regions or objects in an image based on a discontinuity or a similarity criterion.
Which technique is applied for edge segmentation?
The Sobel technique of edge detection for image segmentation finds edges using Sobel approximation derivative [6]. It performs a 2-D spatial gradient measurement on an image and so emphasizes regions of high spatial gradient that corresponds to edges.
What is the objective of image registration?
Roughly speaking, the goal of image registration is to automatically establish correspondences between different images displaying views of objects or organs. These images may be acquired at different times, from different devices or perspectives, or reveal even different types of information.
How many medical devices are there?
Today, there are an estimated 2 million different kinds of medical devices on the world market, categorized into more than 7000 generic devices groups.
Why is automatic segmentation important?
Overall, the proposed auto-segmentation model is equivalent to manual segmentation and is a reliable, consistent, and efficient method for inner ear segmentation which can be used in a variety of clinical applications, such as 3D visualization, surgical planning and quantitative image analysis.
What is the difference between semantic segmentation and instance segmentation?
In other words, semantic segmentation treats multiple objects within a single category as one entity. Instance segmentation, on the other hand, identifies individual objects within these categories. To achieve the highest degree of accuracy, computer vision teams must build a dataset for instance segmentation.
What is auto contouring?
Autocontouring automatically delineates organs at risk, taking away the manual need to draw around these organs. This task is a critical step in preparing a patient for radiation therapy to ensure that healthy organs surrounding the tumor are spared from radiation to improve patient outcomes.
What are the benefits of image segmentation?
The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images.
What is the Atlas-based segmentation process?
The atlas-based segmentation process consists to deform the selected atlas objects in order to better align them with their corresponding objects in the patient image to be segmented. To perform this task, we have distinguished two types of approaches in the literature.
What is digital image segmentation?
Digital image segmentation is an important and recent domain in computer history and digital image processing. It is a type of signal dispensation in which input is image, like video frame or photograph, satellite imagery and output may be image or characteristics associated with that image.
What is a labeled Atlas in image processing?
The labeled atlas aims to delineate objects of interest in the intensity atlas. The atlas-based segmentation process consists to deform the selected atlas objects in order to better align them with their corresponding objects in the patient image to be segmented. To perform this task, we have distinguished two types of approaches in the literature.
How to avoid image segmentation over segmentation using mathematical morphology?
method was proposed for image segmentation using mathematical morphology. The approach was based on the watershed transformation. In order to avoid an over segmentation, they proposed to adapt the topological gr adient method.