TaoH's repositories
awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
awesome-public-datasets
An awesome list of high-quality open datasets in public domains (on-going).
COGS185-AutoContext
My implementation of the Auto Context algorithm
COGS185-RandomForest
My implementation of the Random Forest algorithm, using ID3 decision trees
ConvertVesselTreesToITKSpatialObjects
Convert Vessel Trees To ITK Spatial Objects
Cross_Net
Containing codes for MPhil project of medical image classification and segmentation
cross_validation
Cross validation tests
Curviliniar_Detector
Detects blood vessels, roads, text, etc..
DCNN-Image-Segmentation
Image Segmentation Methods Based on Deep Learning: Study and Implementation
ETH-SegReg
Algorithms and methods for (medical) image registration and segmentation
fcn.berkeleyvision.org
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.
fundus-vessel-segmentation
One of the first steps in automatic fundus image analysis is the segmentation of the retinal vasculature, which provides valuable information related to several diseases. In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. This task remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors, such as a Potts model or total variation. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. The evaluation of our method is performed both quantitatively and qualitatively on DRIVE, STARE, CHASEDB1 and HRF, showing its ability to deal with different types of images and outperforming other techniques, trained using state of the art features.
gco_python
Python wrappers for GCO alpha-expansion and alpha-beta-swaps
GPU-Marching-Cubes
A GPU implementation of the Marching Cubes algorithm for extracting surfaces from volumes using OpenCL and OpenGL
ITKMinimalPathExtraction
http://www.insight-journal.org/browse/publication/213
knn-matting
Source Code for KNN Matting, CVPR 2012 / TPAMI 2013
livewire
Livewire algorithm for image segmentation
LSMLIB
Level Set Method Library
OpenCL-Getting-Started
A small "getting started" tutorial for OpenCL. See http://www.eriksmistad.no/getting-started-with-opencl-and-gpu-computing/ for more info
pysegbase
3D graph cut segmentation
SimpleITK
SimpleITK: a simplified layer build on top of the Insight Toolkit (ITK), intended to facilitate its use in rapid prototyping, education and interpreted languages.
SIPL
The Simple Image Processing Library (SIPL) is a C++ library with the main goal of making it easy to go from an algorithm concept to pictures on the screen.
vessel-tools
Automatically exported from code.google.com/p/vessel-tools
Vessel3DDL
Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images
VesselSeg3d
Vessel (e.g., Coronary) Segmentation from 3D CT volume
VesselView
VesselView is a demonstration application from Kitware Inc for the segmentation, registration and analysis of tubes( e.g, blood vessels) in 3D images (e.g, MRI, CT and Ultrasound).