use level sets with a new approach and are able to segment an image succesfully. The Visualization ToolKit (VTK) is an open source, freely available software system for 3D computer graphics, image processing, and visualization used by thousands of researchers and developers around the world. This is the. This is a fundamental task in computer vision, facial recognition, and medical imaging. I asked google and I've tried to find something in the API of OpenCV, but hadn't any good hints on split and merge image segmentation method. Thresholding: Simple Image Segmentation using OpenCV. Image Segmentation with Python. As the algorithm tries to balance the volume (ie balance the region sizes), if we take circles with different sizes, the segmentation fails. This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The list goes on. A post showing how to perform Upsampling and Image Segmentation with a recently released TF-Slim library and pretrained models. Data re-Shaping in R and in Python; The other day, someone in my office was working a project of Image Segmentation (a topic I know nothing about) for a machine. By replacing the data point with the corresponding cluster’s centroid, we can do segmentation and compression of image. Ask Question Asked 2 months ago. A collection of contours as shown in Figure 1. Before using the current method for classifying an image, the image has to be in register with. Posted on December 9, 2015 January 2, 2018 by weiwen. This article demonstrates the development of code in C# that implements one of the most basic variants of the classical k-means clustering algorithm that can be easily used to perform a simple graphical raster image segmentation. See the following image: On the image there are three objects: a jumping man, the blue sky and the white snow. In contrast, the method described in this paper has been used in large-scale image database applications as described in [13]. Morphological Segmentation runs on any open grayscale image, single 2D image or (3D) stack. import cv2 import numpy as np import matplotlib. For Python, there are very few option for free source RS based image segmentation. Define the set of labels Λ. ) in images. Active 2 months ago. from MIT in this paper. This piece of code shows how to sharpen a grayscale image (for color images do the same for each channel). Image Segmentation with Python. "What's in this image, and where in the image is. The Python Imaging Library adds image processing capabilities to your Python interpreter. How to perform land cover classification using image segmentation in Python? June 28, 2015 | Aaron P. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc. At the core of customer segmentation is being able to identify different types of customers and then figure out ways to find more of those individuals so you can you guessed it, get more customers! In this post, I'll detail how you can use K-Means. Luckily for you, there’s an actively-developed fork of PIL called Pillow – it’s easier to install, runs on all major operating systems, and supports Python 3. The image is successively split into quadrants based on a homogeneity criterion and similar regions are merged to create the segmented result. How to create an efficient algorithm based on the predicate? Greedy algorithm that captures global image features. Image segmentation is the process of partitioning an image to meaningful segments. TF Image Segmentation: Image Segmentation framework. Otherwise, fire up a text editor and create a file named color_segmentation. I am also using scikit-image library and numpy for this tutorial plus other dependencies. Multi-Modal Image Segmentation with Python & SimpleITK. I'll suggest two, Orfeo Toolbox: Probably the best free alternative of eCognition. Color moment feature extraction python. As a non-supervised algorithm, it demands adaptations, parameter tuning and a constant feedback from the developer, therefore, an understanding its concepts is essential to use it effectively. What is Image Segmentation? The goal of image segmentation is to label each pixel of an image with a corresponding class of what is being represented. resize and get hands-on with examples provided for most of the scenarios encountered in regular usage. This paper addresses the problem of segmenting an image into regions. Also included is a suite for variational light field analysis, which. j'ai un probleme les experts de opencv en python je sais pas comment faire ceci: - segmentation (squelletisation) d'image. A nice visual application of mean shift is image segmentation. SIP stands for Scilab Image Processing toolbox. The script can be found on my github, if you’re so inclined. The aim of the TF Image Segmentation framework is to provide/provide a simplified way for: Converting some popular general/medical/other Image Segmentation Datasets into easy-to-use for training. This tutorial focuses on the task of image segmentation, using a modified U-Net. We illustrate the use of three variants of this family of algorithms. 2 Release, including Inference Engine Python API support, samples demonstrating Python API usage, and a demonstration of Python API interoperability between. The task of semantic image segmentation is to classify each pixel in the image. Introduction to image segmentation. The example has several parts. Graph partitioning. Simple image segmentation app. See more: best opencv projects, opencv projects source code, image processing projects using python, image processing using python opencv, image processing python book, python image processing projects, opencv python tutorial pdf, python image processing tutorial pdf, I have attached the project details herewith this project. The main part is a pipeline for the automated segmentation of 2D confocal fluorescence micrographs of membrane-labeled cells. Image Segmentation Algorithms Overview Song Yuheng1, Yan Hao1 (1. I choose Python as it. semantic segmentation is one of the key problems in the field of computer vision. 3d models from 2d image slices. To get started with CNTK we recommend the tutorials in the Tutorials folder. But in the beginning, there was only the most basic type of image segmentation: thresholding. Sometimes we need to identify pixels belonging to different objects. compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. April 22nd 2017. Download the file for your platform. But since your project is called "Classification of breast cancer images with deep learning", and you're not using deep learning, maybe you didn't pick the right. methods of image segmentation (e. An overview of the Segmentation and Classification toolset in Image Analyst. Image Processing with Python. We use a Python-based approach to put together complex. Clownfish are easily identifiable by their bright orange color, so they're a good candidate for segmentation. TF Image Segmentation: Image Segmentation framework. The tutorial will include input and output of MHD images, visualization tricks, as well as uni-modal. In this article i'm going to explain a simple technique for barcode segmentation from images. Morphological Segmentation runs on any open grayscale image, single 2D image or (3D) stack. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is. An excellent textbook on algorithms for image processing for upper-level undergraduate students. Spot Detection Easily write scripts in javascript or python with the. The aim of the TF Image Segmentation framework is to provide/provide a simplified way for: Converting some popular general/medical/other Image Segmentation Datasets into easy-to-use for training. Recently, the algorithm has frequently been used as pre-processing tool to generate oversegmentations or so-called superpixels ‐ groups of. Image segmentation using Morphological operations in Python If we want to extract or define something from the rest of the image, eg. Python Machine Learning Tutorials. Search this site. 26 thoughts to "Simple and effective coin segmentation using Python and OpenCV". Code The image I’m using is the header image of this blog. Using a 4D cardiac time series, you'll determine if a patient is likely to have heart disease. [Python-OpenCV] Create an Image. Homepage of Scilab Image Processing Toolbox. edu Abstract Object detection and multi-class image segmentation are two closely related tasks. OpenCV implemented a marker-based watershed algorithm where we specify which valley points are to be merged and which are not. Image Segmentation is essentially the process of partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. objects of interest (bacteria, fruits, etc. Artificial Intelligence. In addition, as there is no useful information in the intensity of the image, or its gradient, we choose to perform the spectral clustering on a graph that is only weakly informed by the gradient. I m using Opencv and Python. Traditionally, the computer vision / image processing community performed image segmentation based on low-level properties of neighbouring pixels such as color, inte. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. Sharpening is performed by applying a Laplacian operator on the image and adding the output to the original image. Get started. The DCNN performance is however heavily dependent on the availability of large. However, the first step of doing this is identifying where that person is in the source image and this is where Image Segmentation comes into play. In this tutorial, you're going to learn how to implement customer segmentation using RFM(Recency, Frequency, Monetary) analysis from scratch in Python. segmentation. Extract the necessary information from the image and organize this for our computation: The image is represented by large array of pixels with dimension rows by columns by channels — red, green, and blue or RGB. That is, the image shown is what the computations are carried out on --- a grayscale version of the input image (assuming it was a color image). ) in images. Its available inside Python, in QGIS; even has its own gui alternative called Monteverdi. label distinct objects, analyze object shapes). By this, each data point will belong to a cluster. The list goes on. Code The image I’m using is the header image of this blog. This segmentation technique was proposed by Boycov and Jolli in this paper. imshow("Original. Also included is a suite for variational light field analysis, which. use level sets with a new approach and are able to segment an image succesfully. In this post I will show how to use SimpleITK to perform multi-modal segmentation on a T1 and T2 MRI dataset for better accuracy and performance. I asked google and I've tried to find something in the API of OpenCV, but hadn't any good hints on split and merge image segmentation method. It is also classified as a pixel-based image segmentation method since it involves the selection of initial seed points. Sometimes we need to identify pixels belonging to different objects. February 11, 2018 February 23, 2018 / Sandipan Dey. If you are new to TensorFlow Lite and are working with Android or iOS, we recommend exploring the following example applications that can help you get started. Such user interaction results in a robust algorithm for segmentation, but does not preclude complete automation of the process. 3D cut-plane tool for fast post-processing of segmentation results Extensive tutorial and video documentation Compared to other, larger open-source image analysis tools, ITK-SNAP design focuses specifically on the problem of image segmentation, and extraneous or unrelated features are kept to a minimum. The Image Processing and Computer Vision world is too big to comprehend. Image segmentation can be use in real-world applications such as background subtraction, image understanding, and scene labeling. The u-net is convolutional network architecture for fast and precise segmentation of images. The following topics will be covered: Basics of Radiological Image Modalities and their clinical use; Introduction to Medical Image Computing and Toolkits. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. Another advantage is that the watershed transformation requires low computation times in comparison with other segmentation methods. We welcome feedback in the comments and encourage our readers to. Before using the current method for classifying an image, the image has to be in register with. normanfletcher on Image segmentation with openCV & python. October 12, 2019 Sergi Leave a comment. PSO is one of the latest and. Image segmentation is the classification of an image into different groups. The script can be found on my github, if you're so inclined. The following are code examples for showing how to use skimage. where the actual filtering operation must be supplied as a python function One such approach is watershed segmentation. Example code for this article may be […]. 7 in its Anaconda incarnation and OpenCV as image processing library. This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. Various industrial applications like medical, aerial imagery, etc are powered by image segmentation. ; image segmentation, classification, python is an excellent choice for these types of image processing tasks and. Simple image segmentation app. Image Segmentation with Python and SimpleITK. za 2Stellenbosch University, Stellenbosch, South Africa 3Department of Computer Science, University of North Carolina. import cv2 import numpy as np import matplotlib. Image segmentation is the classification of an image into different groups. cvtColor(image, cv2. It's also an image analysis toolkit with a large number of components supporting general filtering operations, image segmentation, and registration. from MIT in this paper. PSO is one of the latest and. Basic Image Segmentation Using Python. Segmentation is the process of splitting an image into two parts, e. Welcome to another OpenCV tutorial. It is not an automatic but an interactive image segmentation. If you are building your application with Python and need to add image processing features to it, there are various libraries you could use. Ask Your Question 1. OpenCV implemented a marker-based watershed algorithm where we specify which valley points are to be merged and which are not. This can be accomplished by clustering the pixels in the image. aircraft-images. Graph partitioning. Features [x] Image annotation for polygon, rectangle, circle, line and point. Image Segmentation¶ Image segmentation is the task of labeling the pixels of objects of interest in an image. Example code for this article may be found at the Kite Github repository. This implementation by default has 4 skip connections (original - 3). What we do is to give different labels for our object we know. It is achieved by localizing the iris-pupil boundary, iris-sclera boundary, eyelids and eyelashes. Image segmentation is "the process of partitioning a digital image into multiple segments. How It Works. Thresholding: Simple Image Segmentation using OpenCV. Felzenszwalb and Huttenlocher's [1] graph-based image segmentation algorithm is a standard tool in computer vision, both because of the simple algorithm and the easy-to-use and well-programmed implementation provided by Felzenszwalb. Now that we have an idea about scikit-image, let us get into details of Image Segmentation. please told. Other examples (semantic segmentation, bbox detection, and classification). The main features of this library are: High level API (just two lines to create neural network) 4 models architectures for binary and multi class segmentation (including legendary Unet) 46 available encoders for each architecture. The Python Imaging Library, or PIL for short, is one of the core libraries for image manipulation in Python. This notebook will illustrate the use of SimpleITK for segmentation of bacteria from a 3D Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) image. The Definition of Salient Objects Unlike fixation datasets, the most widely used salient object segmentation dataset is heavily biased. Level Set Method Part II: Image Segmentation. This piece of code shows how to sharpen a grayscale image (for color images do the same for each channel). All gists Back to GitHub. In this tutorial, we'll be covering thresholding for image and video analysis. Description. Scrutinize and track customer behavior to identify trends and unmet needs. (Section for Image Analysis / Technical University of Denmark) Edge Detection and Image Segmentation - EDISON is a system with a GUI that performs mean shift based discontinuity preserving filtering and image segmentation as well as edge detection with embedded confidence. OpenCV implemented a marker-based watershed algorithm where we specify which valley points are to be merged and which are not. From the last decade, the multi-scale image segmentation is getting a particular interest and practically being used for object-based image analysis. I am using scikit-image. It is an interactive image segmentation. Download the file for your platform. The bi-modal distribution has been destroyed and it is no longer possible to select a single global threshold that will neatly segment the object from its background. Let's see how to find new cluster points to perform better segmentation. Any grayscale image can be viewed as a topographic surface where high intensity denotes peaks and hills while low intensity denotes valleys. If gray, it performs an enhancement; otherwise, it performs a binary classification. Unfortunately, the approach using Otsu’s thresholding is highly dependent on an illumination normalization. where is the spatial location of node , i. But this approach gives you oversegmented result due to noise or any other irregularities in the image. You can learn more about how OpenCV’s blobFromImage works here. The image segmentation approaches can be categorized into two types based on properties of image. The slides on this paper can be found from Stanford Vision Lab. 昨天萌新使用的是PIL这个库,今天发现机器学习也可以这样玩。 视频地址Python机器学习应用. Using a 4D cardiac time series, you'll determine if a patient is likely to have heart disease. This data is generated on a daily basis. Meanshift Algorithm for the Rest of Us (Python) Posted on May 14, 2016 • lo. The following example shows a grayscale image and how this image might be segmented via. In the above image, you can see 4 clusters and their centroids as stars. I thought it would be a helpful, basic look into using scikit image for image segmentation. Project status: Under Development. It is a non-linear operation that converts a gray-scale image into a binary image where the two levels are assigned to pixels that are below or above the specified threshold value. In this article, an implementation of an efficient graph-based image segmentation technique will be described, this algorithm was proposed by Felzenszwalb et. Background. The bi-modal distribution has been destroyed and it is no longer possible to select a single global threshold that will neatly segment the object from its background. Borders Scanning border Removal. DeepLab is a state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e. Code The image I’m using is the header image of this blog. Python Machine Learning Tutorials. Multi-Modal Image Segmentation with Python & SimpleITK. Finally, we will create segmentation masks that remove all voxel except for the lungs. Binary images are a key component of many image processing algorithms. We will be using the COCO dataset for image segmentation. An excellent textbook on algorithms for image processing for upper-level undergraduate students. Manual inspection and visual QC of each segmentation result is not feasible at large scale. [1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa. First, you may convert to gray-scale, but then you have to consider that grayscale still has at least 255 values. The goal of segmentation is typically to locate certain objects of interest which may be depicted in the image. To perform deep learning semantic segmentation of an image with Python and OpenCV, we: Load the model (Line 56). Sometimes we need to identify pixels belonging to different objects. I use daily to process medical images and to create OsiriX and Horos plugins (don't worry I will write about these softwares in future posts). There are, however, a number of fields where images of higher dimensionality must be analyzed. Download the file for your platform. What's Image Segmentation. Readers will learn how to use the image processing libraries, such as PIL, scikit-image, and scipy ndimage in Python, which will enable them to write code snippets in Python 3 and quickly. Image segmentation is a computer vision task in which we label specific regions of an image according to what's being shown. So this is how Image segmentation can be done in Python-OpenCV. A simple image segmentation example in MATLAB. If gray, it performs an enhancement; otherwise, it performs a binary classification. Let's apply this idea to segmentation: if your image has n grey levels, you can group these into k intervals, according to how close they are together. [1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. Here's sample implementation of OpenCV's Expect…. PAMI, 24(5):657–673). The image segmentation can be classified into two basic types: Local segmentation (concerned with specific part or region of image) and Global segmentation (concerned with segmenting the whole image, consisting of large number of pixels). Now that we have an idea about scikit-image, let us get into details of Image Segmentation. The examples are structured by topic into Image, Language Understanding, Speech, and so forth. Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. Many researches have been done in the area of image segmentation using clustering. There are many segmentation algorithms available, but nothing works perfect in all the cases. For Image processing Python with OpenCV library is widely used. More details on a variety of image segmentation For example, assuming you have the three bands read into python as. K-Means Clustering Video by Siraj Raval; K-Means Clustering Lecture Notes by Andrew Ng; K-Means Clustering Slides by David Sontag (New York University) Programming Collective Intelligence Chapter 3. In this post I will show how to use SimpleITK to perform multi-modal segmentation on a T1 and T2 MRI dataset for better accuracy and performance. pb and frozen_inference_graph. Example code for this article may be found at the Kite Github repository. Each pixel can be viewed as a vector in a 3-d space and say for a 512×512 image, we would be having 1024 such vectors. In this study, we have addressed the issues on multi-scale image segmentation, especially, in improving the performances for validity of merging and variety of derived region's shape. The dataset consists of images, their corresponding labels, and pixel-wise masks. Image segmentation using Morphological operations in Python If we want to extract or define something from the rest of the image, eg. Machine learning is a field of computer science that uses statistical techniques to give computer programs the ability to learn from past experiences and improve how they perform specific tasks. Skip to content. In this paper, we propose a method for image segmentation by computing similarity coefficient in RGB color Color Image Segmentation Using Fast Density-Based Clustering Method | SpringerLink. The Tutorials/ and Examples/ folders contain a variety of example configurations for CNTK networks using the Python API, C# and BrainScript. After that, in line 12, we used classcv2. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. md Modern remote sensing image processing with Python - modern-geospatial-python. In this note, I'll describe how to implement a region growing method for 3D image volume segmentation (note: the code here. See more: best opencv projects, opencv projects source code, image processing projects using python, image processing using python opencv, image processing python book, python image processing projects, opencv python tutorial pdf, python image processing tutorial pdf, I have attached the project details herewith this project. Check out the video below where I am trying out my own Invisibility Cloak!. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc. There are different methods and one of the most popular methods is k-means clustering algorithm. Converting an image file to a pixel matrix. I thought it would be a helpful, basic look into using scikit image for image segmentation. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. Posted on December 9, 2015 January 2, 2018 by weiwen. Text recognition of an image is a very useful step to get the recovery of multimedia content. The ENet model we are using in this blog post was trained on input images with 1024×512 resolution — we’ll use the same here. Define the set of labels Λ. Recent advances in machine learning, especially deep learning, have enabled more sophisticated methods of image segmentation that involve almost no manual tuning of parameters. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. Since you are "learning python and image processing with python", it seems you picked some related methods to explore, which is good. Because we're predicting for every pixel in the image, this task is commonly referred to as dense prediction. SimpleITK is an abstraction layer and wrapper around the Insight Segmentation and Registration Toolkit (). En effet, j'ai utilisé les structures de données suivantes: -3 tableaux rouge , vert , bleu pour stocker les valeurs rgb. What's Image Segmentation. There are many segmentation algorithms available, but nothing works perfect in all the cases. py Stanford CS231A 31. Many researches have been done in the area of image segmentation using clustering. This library implements a number of segmentation algorithms including quickshift and slick, which are what we use in this tutorial. Our process is to denoise the image (using a median filter), and to then apply watershed segmentation. Consider the following photo that I took recently (largely because the nice color variation makes it a nice example. DeepLab is a state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e. Introduction. To be meaningful and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Analysis of images. But in the beginning, there was only the most basic type of image segmentation: thresholding. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. The image segmentation technique here is performed by identifying a region of interest (ROI) and creating a mask that will be used to isolate that region from the remainder of the image. Converting an image file to a pixel matrix. Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain. Next, we convert the mask to grayscale using cv2 (python's bindings to OpenCV), so we now have a. , [14, 16]), these methods are too slow to be practical for many applications. segmentation. What are the shapes of your objects?. md Modern remote sensing image processing with Python - modern-geospatial-python. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. [Python-OpenCV] Create an Image. Introduction. The image is successively split into quadrants based on a homogeneity criterion and similar regions are merged to create the segmented result. 图像分割:利用图像的灰度、颜色、纹理、形状等特征,把图像分成若 干个互不重叠的区域,并使这些特征在同一区域内呈现相似性,在不同的区 域之间存在明显的差异性。. from MIT in this paper. OpenCV-Python Tutorials » Image Processing in OpenCV It is an interactive image segmentation. Image segmentation is the classification of an image into different groups. At the core of customer segmentation is being able to identify different types of customers and then figure out ways to find more of those individuals so you can you guessed it, get more customers! In this post, I'll detail how you can use K-Means. But this approach gives you oversegmented result due to noise or any other irregularities in the image. However, for the dense prediction task of image segmentation, it's not immediately clear what counts as a "true positive" and, more generally, how we. 5 More Examples Exercises Appendix Installing Packages. Let's apply this idea to segmentation: if your image has n grey levels, you can group these into k intervals, according to how close they are together. Various industrial applications like medical, aerial imagery, etc are powered by image segmentation. Python: retval = cv. In this three part series, we walked through the entire Keras pipeline for an image segmentation task. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. There are, however, a number of fields where images of higher dimensionality must be analyzed. The code was written to be trained using the BRATS data set for brain tumors, but it can be easily modified to be used in other 3D applications. Chapter 9 Image Segmentation 9. Image Segmentation. This helps in understanding the image at a much lower level, i. Starting from user-defined markers, the watershed algorithm treats pixels values as a local topography (elevation). I need to segment a set of anisotropic 3D images - confocal images of DAPI staining of zygotes. So this is how Image segmentation can be done in Python-OpenCV. This article is a comprehensive overview including a step-by-step guide to implement a deep learning image segmentation model. , the coordinates in the original image , and is a feature vector defined as: for segmenting point sets,, the intensity value, for segmenting brightness (gray scale) images,, where are the HSV values, for color segmentation,. The ENet model we are using in this blog post was trained on input images with 1024×512 resolution — we'll use the same here. Use hand-crafted features computed on small image patches centered on each pixel (integral images are your friend if you want efficiency!). Historical Introduction and Background Segmentation is one of the fundamental problems in biomedical image analysis and refers to the process of tag-ging image pixels or voxels with biologically meaningful. ; image segmentation, classification, python is an excellent choice for these types of image processing tasks and. These images should be the same size as the benchmark images (481x321 pixels), and should be named. Check that most of the contours aren't bigger in size than 50. The approach is implemented in Python and OpenCV and extensible to any image segmentation task that aims to identify a subset of visually distinct pixels in an image. This repository contains a collection of Jupyter Notebooks illustrating the use of SimpleITK for educational and research activities. The DCNN performance is however heavily dependent on the availability of large. Simple image segmentation app. 3D cut-plane tool for fast post-processing of segmentation results Extensive tutorial and video documentation Compared to other, larger open-source image analysis tools, ITK-SNAP design focuses specifically on the problem of image segmentation, and extraneous or unrelated features are kept to a minimum.