Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

METHOD TO EXTRACT BLEND SURFACE FEATURE IN REVERSE ENGINEERING

A new method of extraction of blend surface feature is presented. It contains two steps: segmentation and recovery of parametric representation of the blend. The segmentation separates the points in the blend region from the rest of the input point cloud with the processes of sampling point data, estimation of local surface curvature properties and comparison of maximum curvature values. The recovery of parametric representation generates a set of profile curves by marching throughout the blend and fitting cylinders. Compared with the existing approaches of blend surface feature extraction, the proposed method reduces the requirement of user interaction and is capable of extracting blend surface with either constant radius or variable radius. Application examples are presented to verify the proposed method.

Morphological features of allogenic nerve segment in rats after subcutaneous embedment

BACKGROUND ： Some studies demonstrate that allogenic peripheral nerve segment embedded subcutaneously significantly reduce the infiltration of lymphocyte and decrease immunological reaction.OBJECTIVE ： To observe the gross shape, optical and electron microscope results of allogenic nerve segment in rats 2 weeks after subcutaneous embedment, and compare with subcutaneous emdedment of autologous nerve segment. DESIGN ： A randomized and controlled experiment.SETTING ： Department of Orthopaedics of Fifth People＇s Hospital of Zhengzhou; Department of Orthopaedics,First Hospital Affiliated to Chongqing Medical University.MATERIALS ： Totally 30 adult healthy Wistar male rats, with body mass of （200±20） g, were enrolled. Ten rats were chosen as the donors of allogenic nerve transplantation. The other 20 rats were randomly divided into 2 groups： allogenic nerve embedment group and autologous nerve embedment group, with 10 rats in each one. JEM-1220 transmission electron microscope （Japan） and Olympus BX50 optical microscope （Japan） were used. METHODS ： This experiment was carried out at the laboratory of Orthopaedic Department, Chongqing Medical University from October 2000 to April 2002. ① Sciatic nerve of donor rats for allogenic nerve transplantation was cut off at 5 mm distant from pelvic strait.15 mm sciatic nerve segment was chosen from lateral part as graft, allogenic nerve embedment group： 15 mm sciatic nerve form the donor rats was embedded in the posterior part of right legs. Autologous nerve embedment group： 15 mm sciatic nerve segment of autologous left side was embedded in the posterior side of right legs. ② Nerve segment embedded subcutaneously was taken out at postoperative 2 weeks and performed gross observation; then 5 samples chosen randomly respectively from 2 groups and given haematoxylin-eosin staining and observation under optical microscope （×400）;The other 5 samples were made into ultrathin sections （0.5μm）and observed under transmission electron microscope（×17 000）. MAIN OUTCOME MEASURES ： Gross shape, optical and electron microscope results of nerve segments of rats between two groups at 2 weeks after subcutaneous embedment. RESULTS ： ① Results of gross observation： Appearance of nerve segment was similar between 2 groups. ② Results of optical observation： medullary sheath denaturation, axonotmesis, vascular engorgement, desmoplasia of adventitia and infiltration of inflammatory cells were all found in both 2 groups. Inflammatory reaction was a little more severe in the allogenic nerve embedment group than in the autologous nerve embedment groups.③Results of electron microscope ： Similar cataplasia and denaturation of medullary sheath and cataplasia of Schwann cell were all found in the 2 groups. CONCLUSION： Some inflammatory reaction occurs after allogenic nerve embedment, but the activity of Schwann cell is similar to that of peripheral nerve after autologous nerve embedment.

Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed.The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire interferences.Secondly,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved.Then,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame.Finally,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were obtained.The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.

Characteristics of the May 12,2008 Wenchuan M_S8.0 Earthquake Sequence and Discussion on Relevant Problems

The aftershock activity of the May 12, 2008 Wenchuan Ms8.0 Earthquake Sequence shows an obvious segmented feature. Most of the large aftershocks were distributed in the north and south parts of the aftershock zone. Thrusting was dominant with a small amount of strike-slip component in the south part. The aftershock activity decayed gradually, presenting the sequence features of a mainshock-aftershock pattern. The north part was the ending area of the malnshock fracture where strike-slipping was dominant, showing an obvious swarm feature. Therefore it became the major area for large aftershocks. The modulation of the earth tide on aftershock activity is remarkable; most large aftershocks occur during the period of flood and neap tide. The time period around 16：00 was the dominant occurring time for large aftershocks. The p-value, a parameter of modified Omori formula, increases gradually with time, and reaches about 1 at the end. Based on previous study, the sequence patterns, magnitude of maximum aftershock, as well as the duration of aftershock activity has been discussed. The primary results also show that the magnitude difference between the maiushock and the maximum aftershock is proportional to the rupture size of the maiushock for huge earthquakes of about Ms8.0. This means that when the magnitudes of the earthquakes are nearly the same, large rupture size corresponds to sufficient energy release.

Three-dimensional Geometry and Kinematics of the Changning Anticline in the Southern Sichuan Basin

In this study,we aim to clarify the structural characteristics and deformation process of the Changning anticline.We carefully interpret 38 two-dimensional(2D)seismic profiles in the study area and establish three-dimensional(3D)geometric and quantitative kinematic models of the Changning anticline.This study shows that the basement fault controls the formation of the Changning anticline.The fault slope of the main fault in the basement shows’steep in the upper and gentle in the lower’structural characteristics vertically,possessing obvious segmentary characteristics transversely and presents the overall characteristics of’steep in the east and gentle in the west’.Further analysis shows that the Changning anticline proceeds west and terminates at the boundary defined by current surface features but gradually disappears westward across the Mt.Huaying fault zone.Furthermore,we identified that deformation of the Changning anticline began during the early Yanshanian movement period.Under compressional stress from the southeast,the anticline slid forward along the basement fault until the end of the Yanshanian movement period,when the dominant WNW-ESE structure gradually emerged.Since the Himalayan movement period,a series of NE-trending structures have been formed in the anticline,owing to multi-directional compressive stress.

A Brain-inspired SLAM System Based on ORB Features

This paper describes a brain-inspired simultaneous localization and mapping （SLAM） system using oriented features from accelerated segment test and rotated binary robust independent elementary （ORB） features of RGB （red, green, blue） sensor for a mobile robot. The core SLAM system, dubbed RatSLAM, can construct a cognitive map using information of raw odometry and visual scenes in the path traveled. Different from existing RatSLAM system which only uses a simple vector to represent features of visual image, in this paper, we employ an efficient and very fast descriptor method, called ORB, to extract features from RCB images. Experiments show that these features are suitable to recognize the sequences of familiar visual scenes. Thus, while loop closure errors are detected, the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm. Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.g.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.