Segmentation of confocal microscopic image of insect brain

نویسندگان

  • Ming-Jin Wu
  • Chih-Yang Lin
  • Yu-Tai Ching
چکیده

Accurate analysis of insect brain structures in digital confocal microscopic images is valuable and important to biology research needs. The first step is to segment meaningful structures from images. Active contour model, known as snakes, is widely used for segmentation of medical images. A new class of active contour model called gradient vector flow snake has been introduced in 1998 to overcome some critical problems encountered in the traditional snake. In this paper, we use gradient vector flow snake to segment the mushroom body and the central body from the confocal microscopic insect brain images. First, an edge map is created from images by some edge filters. Second, a gradient vector flow field is calculated from the edge map using a computational diffusion process. Finally, a traditional snake deformation process starts until it reaches a stable configuration. User interface is also provided here, allowing users to edit the snake during deformation process, if desired. Using the gradient vector flow snake as the main segmentation method and assist with user interface, we can properly segment the confocal microscopic insect brain image for most of the cases. The identified mushroom and central body can then be used as the preliminary results toward a 3-D reconstruction process for further biology researches.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation

Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...

متن کامل

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

Background: Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging.Objective: This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regiona...

متن کامل

Segmentation of Magnetic Resonance Brain Imaging Based on Graph Theory

Introduction: Segmentation of brain images especially from magnetic resonance imaging (MRI) is an essential requirement in medical imaging since the tissues, edges, and boundaries between them are ambiguous and difficult to detect, due to the proximity of the brightness levels of the images. Material and Methods: In this paper, the graph-base...

متن کامل

A Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis

Accurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In this research, a novel fuzzy approach has been proposed to classify a given MRI brain image as normal or cancer label and the i...

متن کامل

A hierarchical Convolutional Neural Network for Segmentation of Stroke Lesion in 3D Brain MRI

Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2003