Retinal Vessel Segmentation Based on Adaptive Random Sampling

International audienceThis paper presents a method for the extraction of blood vessels from fundus images. The proposed method is an unsupervised learning method which can automatically segment retinal blood vessels based on an adaptive random sampling algorithm. This algorithm consists in taking an adequate number of random samples in fundus images, and all the samples are contracted to the position of the blood vessels, then the retinal vessels will be revealed. The basic algorithm framework is presented in this paper and several preliminary experiments validate the feasibility and effectiveness of the proposed method

Segmenting video frame images using genetic algorithms

Image segmentation plays an important role in computer vision. It is a process that partitions a digital image into several meaningful regions ,by identifying regions of an image that have common properties while separating regions that are dissimilar. The image segmentation problem is posed as an optimization procedure. In this thesis, an optimization approach based on genetic algorithm is introduced for finding optimal image segmentation. The design and implementation of genetic algorithm image segment or (GSAI) system are described. GSAI system employs finds optimal value using genetic operators "crossover operator and mutation operator". The different proposed / implementation segmentation methods of the GSAI system were tested using Gray image are taken from one films and with size 352x240 pixels for video frames images of In this is work focused on genetic algorithm coefficients which affect in direct and active way in the work of GA to study and analysis dependable video images which are taken from video clips after partitioning to multiple frames

A tool for the quantification of radial neo-vessels in chick chorioallantoic membrane angiogenic assays

Angiogenesis, the process of new blood vessels formation, plays a key role in different physiological and pathological conditions and it is considered a promising target for the development of new anti-inflammatory and anti-tumor therapies. Several assays have been developed to mimic the angiogenic process in vitro and in vivo. Here we propose a technique for the quantification of the pro-angiogenic or anti-angiogenic responses induced by different molecules when implanted in vivo on the chick embryo chorioallantoic membrane (CAM). At day 11 of development CAM is completely vascularized and neo-vessels induced by exogenous molecules converge radially to the implant. Our algorithm is an effective and rapid tool to characterize molecules endowed with proor anti-angiogenic effects by means of the quantification of the vessels present in the CAM macroscopic images. Based on conventional and dedicated image morphology tools, the proposed technique is able to discriminate radial from non-radial vessels, excluding the last ones from the count

Evaluation of Publicly Available Blood Vessel Segmentation Methods for Retinal Images

Retinal blood vessel structure is an important indicator of disorders related to diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and two unsupervised retinal blood vessel segmentation methods are quantitatively compared by using five publicly available databases with the ground truth for the vessels. The parameters of each method were optimized for each database with the motivation to achieve good segmentation performance for the comparison and study the importance of proper selection of parameter values. The results show that parameter optimization does not significantly improve the segmentation performance of the methods when the original data is used. However, the methods’ performance for new data differs significantly. Based on the comparison, Soares method as a supervised approach provided the highest overall accuracy and, thus, the best generalisability. Bankhead and Nguyen methods’ performance were close to each other: Bankhead performed better with ARIADB and STARE, whereas Nguyen was better with DRIVE. Sofka method is available only as an executable and its performance matched the others only with ARIADB

Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not inuenced by inter-subject variability

Medical Image Segmentation using Deep Learning using Segnet

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these problems, we propose a novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine-tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal Magnetic Resonance (MR) slices, where only two types of these organs were annotated for training; and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only the tumor core in one MR sequence was annotated for training. Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image- specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods