Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.

Kinematic Analysis of Persian Joint Using 3D Rotation Matrix

This paper has been done on study kinematic problem of Persian joint in a general way. In this study, instead of using simulation analysis method as in the previous researches, the 3D rotation matrix method is applied to present the relationship of angular velocities of input shaft and output shaft. The result shows that when the angle between intersecting shafts changes from 0 to 135°, the angular velocity is maintained constant. This new result completely matches with analysis from kinematic simulation of this mechanism. The obtained result is an important base to solve dynamic problem in order to develop the applicability of this joint in reality.

3D Gray Level Co-Occurrence Matrix Based Classification of Favor Benign and Borderline Types in Follicular Neoplasm Images

Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.

Nanoengineered hydrogels as 3D biomimetic extracellular matrix with injectable and sustained delivery capability for cartilage regeneration

The regeneration of articular cartilage remains a great challenge due to the difficulty in effectively enhancing spontaneous healing.Recently,the combination of implanted stem cells,suitable biomaterials and bioactive molecules has attracted attention for tissue regeneration.In this study,a novel injectable nanocomposite was rationally designed as a sustained release platform for enhanced cartilage regeneration through integration of a chitosan-based hydrogel,articular cartilage stem cells(ACSCs)and mesoporous SiO_(2)nanoparticles loaded with anhydroicaritin(AHI).The biocompatible engineered nanocomposite acting as a novel 3D biomimetic extracellular matrix exhibited a remarkable sustained release effect due to the synergistic regulation of the organic hydrogel framework and mesopore channels of inorganic mSiO_(2)nanoparticles(mSiO_(2)NPs).Histological assessment and biomechanical tests showed that the nanocomposites exhibited superior performance in inducing ACSCs proliferation and differentiation in vitro and promoting extracellular matrix(ECM)production and cartilage regeneration in vivo.Such a novel multifunctional biocompatible platform was demonstrated to significantly enhance cartilage regeneration based on the sustained release of AHI,an efficient bioactive natural small molecule for ACSCs chondrogenesis,within the hybrid matrix of hydrogel and mSiO_(2)NPs.Hence,the injectable nanocomposite holds great promise for use as a 3D biomimetic extracellular matrix for tissue regeneration in clinical diagnostics.

Spatiotemporal emotion recognition based on 3D time-frequency domain feature matrix

The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional states.Aiming at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was proposed.Specifically,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral cortex.Then,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional state.Spatiotemporal emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification tasks.The experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.