CE-EEN-B0:Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

A brain tumor is the uncharacteristic progression of tissues in the brain.These are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life span.Hence,their classification and detection play a critical role in treatment.Traditional Brain tumor detection is done by biopsy which is quite challenging.It is usually not preferred at an early stage of the disease.The detection involvesMagneticResonance Imaging(MRI),which is essential for evaluating the tumor.This paper aims to identify and detect brain tumors based on their location in the brain.In order to achieve this,the paper proposes a model that uses an extended deep Convolutional Neural Network(CNN)named Contour Extraction based Extended EfficientNet-B0(CE-EEN-B0)which is a feed-forward neural network with the efficient net layers;three convolutional layers and max-pooling layers;and finally,the global average pooling layer.The site of tumors in the brain is one feature that determines its effect on the functioning of an individual.Thus,this CNN architecture classifies brain tumors into four categories:No tumor,Pituitary tumor,Meningioma tumor,andGlioma tumor.This network provides an accuracy of 97.24%,a precision of 96.65%,and an F1 score of 96.86%which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image.This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

Fast and Robust DCNN Based Lithography SEM Image Contour Extraction Models

Scanning electron microscope(SEM)metrology is critical in semiconductor manufacturing for patterning process quality assessment and monitoring.Besides feature width and feature-feature space dimension measurements from critical dimension SEM(CDSEM)images,visual inspection of SEM image also offers rich information on the quality of patterning.However,visual inspection alone leaves considerable room of ambiguity regarding patterning quality.To narrow the room of ambiguity and to obtain more statistically quantitative information on patterning quality,SEM-image contours are often extracted to serve such purposes.From contours,important information such as critical dimension and resist sidewall angle at any location can be estimated.Those geometrical information can be used for optical proximity correction(OPC)model verification and lithography hotspot detection,etc.Classical contour extraction algorithms based on local information have insufficient capability in dealing with noisy and low contrast images.To achieve reliable contours from noisy and low contrast images,information beyond local should be made use of as much as possible.In this regard,deep convolutional neural network(DCNN)has proven its great capability,as manifested in various computer vision tasks.Taking the full advantages of this maturing technology,we have designed a DCNN network and applied it to the task of extracting contours from noisy and low contrast SEM images.It turns out that the model is capable of separating the resist top and bottom contours reliably.In addition,the model does not generate false contours,it also can suppress the generation of broken contours when ambiguous area for contour extraction is small and non-detrimental.With advanced image alignment algorithm with sub-pixel accuracy,contours from different exposure fields of same process condition can be superposed to estimate process variation band,furthermore,stochastic effect induced edge placement variation statistics can easily be inferred from the extracted contours.

Edge Contour Extraction in MR Image Using Edgeflow Contour Model

This paper presents a new model for edge extraction of MR images, based on curve evolution and edgeflow techniques. At first the model for curve evolution is constructed, which automatically detect boundaries, and change of topology in terms of the edgeflow fields, and then the numerical approximation of the model is introduced, which is based on semi-implicit scheme to speed up the proposed approach. Finally, the numerical implementation is present and the experimental results show that the proposed model successfully extracts the edge contours, regardless of the heavy noise.

Contour reconstruction of three-dimensional spiral CT damage image

In order to improve the diagnosis and analysis ability of 3D spiral CT and to reconstruct the contour of 3D spiral CT damage image,a contour reconstruction method based on sharpening template enhancement for 3D spiral CT damage image is proposed.This method uses the active contour LasSO model to extract the contour feature of the 3D spiral CT damage image and enhances the information by sharpening the template en.hancement technique and makes the noise separation of the 3D spiral CT damage image.The spiral CT image was procesed with ENT,and the statistical shape model of 3D spiral CT damage image was established.The.gradient algorithm is used to decompose the feature to realize the analysis and reconstruction of the contour feature of the 3D spiral CT damage image,so as to improve the adaptive feature matching ability and the ability to locate the abnormal feature points.The simulation results show that in the 3D spiral CT damage image contour reconstruction,the proposed method performs well in the feature matching of the output pixels,shortens the contour reconstruction time by 20/ms,and provides a strong ability to express the image information.The normalized reconstruction error of CES is 30%,which improves the recognition ability of 3D spiral CT damage image,and increases the signal-to noise ratio of peak output by 40 dB over other methods.

A Holistic Approach for Efficient Contour Detection

Object contours contain important visual information which can be applied to numerous vision tasks. As recent algorithms focus on tile accuracy of contour detection, the entailed time complexity is significantly high. In this paper, we propose an efficient and effective contour extraction method based on both local cues from pixels and global cues from saliency. Experimental results demonstrate that a good trade-off between accuracy and speed can be achieved by the proposed approach for contour detection.

Development of a monocular vision system for robotic drilling

Robotic drilling for aerospace structures demands a high positioning accuracy of the robot, which is usually achieved through error measurement and compensation. In this paper, we report the development of a practical monocular vision system for measurement of the relative error between the drill tool center point(TCP) and the reference hole. First, the principle of relative error measurement with the vision system is explained, followed by a detailed discussion on the hardware components, software components, and system integration. The elliptical contour extraction algorithm is presented for accurate and robust reference hole detection. System calibration is of key importance to the measurement accuracy of a vision system. A new method is proposed for the simultaneous calibration of camera internal parameters and hand-eye relationship with a dedicated calibration board. Extensive measurement experiments have been performed on a robotic drilling system. Experimental results show that the measurement accuracy of the developed vision system is higher than 0.15 mm, which meets the requirement of robotic drilling for aircraft structures.

Face Live Detection Method Based on Physiological Motion Analysis

In recent years, face recognition has often been proposed for personal identification. However, there are many difficulties with face recognition systems. For example, an imposter could Iogin the face recognition system by stealing the facial photograph of a person registered on the facial recognition system. The secudty of the face recognition system requires a live detection system to prevent system Iogin using photographs of a human face. This paper describes an effective, efficient face live detection method which uses physiological motion detected by estimating the eye blinks from a captured video sequence and an eye contour extraction algorithm. This technique uses the conventional active shape model with a random forest classifier trained to recognize the local appearance around each landmark. This local match provides more robustness for optimizing the fitting procedure. Tests show that this face live detection approach successfully discriminates a live human face from a photograph of the registered person＇s face to increase the face recognition system reliability.

Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction

Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods.The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging(MRI)images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models.Furthermore,the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax.The proposed modeling method is fast and accurate,and it is universal for different types of MRI images.The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB.The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model,the 2.5D model and other human chest models.