Adopting level set theory based algorithms to segment human ear

Perspective methods of human identification: Ear biometrics

An ear biometric system based on artificial bees and the scale invariant feature transform

This paper presents a framework that uses ear images for human identification. The framework makes use of Principal Component Analysis (PCA) for ear image feature extraction and Multilayer Feed Forward Neural Network for classification. Framework are proposed to improve recognition accuracy of human identification. The framework was tested on an ear image database to evaluate its reliability and recognition accuracy. The experimental results showed that our framework achieved higher stable recognition accuracy and over-performed other existing methods. The recognition accuracy stability and computation time with respect to different image sizes and factors were investigated thoroughly as well in the experiments.

Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics but in case of ear biometrics the shape and appearance is fixed. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Image segmentation method based on level set model has wide potential application for its excellent seg-mentation result. However its complex computing restricts its application in video segmentation. In order to improve the speed of image segmentation, this paper presents a new level set initialization method based on Chan-Vese level set model. After a simple iterative, we can separate out the outline of objects. Experiments show that the method is simple and efficient, with good separation effects. The improved Chan-Vese method can be applied in video segmentation.

Image segmentation is one of the most fundamental and important areas in the field of image processing and computer vision. The traditional level set methods need initialize the level set function as a distance function. If the initial contour is selected inappropriate, we may not get the desired ideal segmentation result. In order to solve the problem of level set automation initial, we proposed a new image segmentation algorithm based on level set and marker extraction. First, we extract the internal mark as level set initial curve by using Extended-minima transform. And then, through using the local binary fitting active contour model, we evolve the labeled image to get the final segmentation result. The simulation results show that this method has low computing complexity than the traditional level set method requirements, and can effectively solve the initialization problem of level set.

Color image segmentation has been widely applied to diverse fields in the past decades for containing more information than gray ones. The traditional level set algorithm calculates the level set energy function based on the gray value of the gray image. A level set algorithm based on color space is proposed, which is based on weightings of different channels, and the energy function of the level set is rewritten. The k-means algorithm is used to pre-segment, initialize the level set contour curve and the sign distance function, and achieve the unsupervised segmentation of the algorithm. The steps of the algorithm are given. The adaptability and accuracy of different types of color image are solved. Compared to other popular algorithms, it has the competitive performances both on speed and accuracy. The experiments performed on real-world data sets demonstrate the validity of the proposed algorithm.

Its a research hotspot recently to conduct image analysis and target detection using the object image acquired by the Synthetic Aperture Radar (SAR) in military survey, military mapping and ocean monitoring. To be able to quickly and accurately extract the object contour of SAR image real sequence captured by the aircraft in the low dynamic near space, the level set method is introduced in this article based on separating object from background by Difference Image Method. Then, the level set method is improved with the change of object image time sequence to accelerate contour extraction. The simulation experiment proves this method is efficient.

The variational level set model for piecewise constant multiphase image segmentation on the plane and the related Split-Augmented-Lagrangian Projection Method (SALPM) are investigated in this paper. On the analysis of the current problems based on the variational level set method for image segmentation, we also design a rapid SALPM method for two-phase image segmentation model, getting the general model of multiple phase level set in order to facilitate the generic design and program. In addition, the concrete formula of the rapid split algorithms for multiphase image segmentation with level set model and the calculation steps are given, and simultaneously taking liver tumor CT image as examples of the multiphase segmentation. Moreover, by comparing with the traditional methods, the experiments show that our algorithm presented in this paper have higher computational efficiency and accuracy, and better the extraction of liver contour.

Research on urban spatial structure based on the dual constraints of geographic environment and POI big data

Accurate segmentation of ovarian cancer metastases is clinically useful to evaluate tumor growth and determine follow-up treatment. We present a region-based level set algorithm with localization constraints to segment ovarian cancer metastases. Our approach is established on a representative region-based level set, Chan-Vese model, in which an active contour is driven by region competition. To reduce over-segmentation, we constrain the level set propagation within a narrow image band by embedding a dynamic localization function. The metastasis intensity prior is also estimated from image regions within the level set initialization. The localization function and intensity prior force the level set to stop at the desired metastasis boundaries. Our approach was validated on 19 ovarian cancer metastases with radiologist-labeled ground-truth on contrast-enhanced CT scans from 15 patients. The comparison between our algorithm and geodesic active contour indicated that the volume overlap was 75±10% vs. 56±6%, the Dice coefficient was 83±8% vs. 63±8%, and the average surface distance was 2.2±0.6mm vs. 4.4±0.9mm. Experimental results demonstrated that our algorithm outperformed traditional level set algorithms.

Regional level set method is a popular approach for image segmentation that uses inside and outside information of contour to extract object boundary. Unfortunately, in many cases, such method is not adequate to model complex textured objects. In this paper, we propose a natural texture image segmentation method which incorporates the pixel-level feature into region-level feature. The multiscale local structure operation is proposed as pixel-level feature to describe the texture structure of image. So the problems of multi-scale and rotation invariance of inhomogeneous texture are addressed by introducing multi-scale local structure operation into level set energy functional. Then, the global intensity information is extracted as the region-level feature and integrated with multi-scale local structure operation. Further, we propose a so-called vector level set method to obtain the segmentation results. Here, we extend the traditional regional level set model into the vector formulation so that the multi-scale local structure operation can be suitably combined with the global intensity information to achieve the more superior image segmentation performance than that of the traditional segmentation methods for texture images. Experiments on some synthesis texture images and real natural scene images demonstrate the excellent performance of the proposed method which successfully combines local structure information and global intensity information to extract the object boundary.

Early detection of breast tumors, feet pre-ulcers diagnosing in diabetic patients, and identifying the location of pain in patients are essential to physicians. Hot or cold regions in medical thermographic images have potential to be suspicious. Hence extracting the hottest or coldest regions in the body thermographic images is an important task. Lazy snapping is an interactive image cutout algorithm that can be applied to extract the hottest or coldest regions in the body thermographic images quickly with easy detailed adjustment. The most important advantage of this technique is that it can provide the results for physicians in real time readily. In other words, it is a good interactive image segmentation algorithm since it has two basic characteristics: (1) the algorithm produces intuitive segmentation that reflects the user intent with given a certain user input and (2) the algorithm is efficient enough to provide instant visual feedback. Comparing to other methods used by the authors for segmentation of breast thermograms such as K-means, fuzzy c-means, level set, and mean shift algorithms, lazy snapping was more user-friendly and could provide instant visual feedback. In this study, twelve test cases were presented and by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30s for these twelve cases. It was concluded that lazy snapping was much faster than other methods applied by the authors such as K-means, fuzzy c-means, level set, and mean shift algorithms for segmentation. Graphical abstract Time taken to implement lazy snapping algorithm to extract suspicious regions in different presented thermograms (in seconds). In this study, ten test cases are presented that by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30s for the ten cases. It concludes lazy snapping is much faster than other methods applied by the authors.

TMS brain mapping in less than two minutes.