Automatic Tumor Detection Research Articles

Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma.

Simple SummaryHepatocellular carcinoma is one of the leading causes of cancer-related deaths worldwide. An image fusion system is developed for the robotic-assisted treatment of hepatocellular carcinoma, which is not only capable of imaging data interpretation and reconstruction, but also automatic tumor detection. The optimization and integration of the image fusion system within a novel robotic system has the potential to demonstrate the feasibility of the robotic-assisted targeted treatment of hepatocellular carcinoma by showing benefits such as precision, patients safety and procedure ergonomics.Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a novel visualization and detection system for HCC, which is a composing module of a robotic system for the targeted treatment of HCC. The system has two modules, one for the tumor visualization that uses image fusion (IF) between computerized tomography (CT) obtained preoperatively and real-time ultrasound (US), and the second module for HCC automatic detection from CT images. Convolutional neural networks (CNN) are used for the tumor segmentation which were trained using 152 contrast-enhanced CT images. Probabilistic maps are shown as well as 3D representation of HCC within the liver tissue. The development of the visualization and detection system represents a milestone in testing the feasibility of a novel robotic system in the targeted treatment of HCC. Further optimizations are planned for the tumor visualization and detection system with the aim of introducing more relevant functions and increase its accuracy.

PO-1551: Deep CNN on PET/CT images for NSCLC automated tumor detection and outcome prediction

PO-1551: Deep CNN on PET/CT images for NSCLC automated tumor detection and outcome prediction

Brain Tumor Extraction from MRI Images Using MATLAB

Computerized or Automatic detection of tumors in medical images is motivated by the necessity of high accuracy when dealing with a human life. The computer assistance is also demanded in medical institutions because it could improve the results of disease identification and negative cases should be very low. So, the Processing of Magnetic Resonance Imaging (MRI) images is one of the techniques to diagnose the brain tumor. This paper describes the strategy to detect and extract brain tumor from patient’s MRI scanned images. In this the Steps includes are pre-processing, segmentation, morphological operation, watershed segmentation and calculation of the tumor area and determination of the tumor location and this Application is Developed using Matrix Laboratory (MATLAB)

Brain Tumor Detection from MR Images Employing Fuzzy Graph Cut Technique

Background: This research aims at the accurate selection of the seed points from the brain MRI image for the detection of the tumor region. Since, the conventional way of manual seed selection leads to inappropriate tumor extraction therefore, fuzzy clustering technique is employed for the accurate seed selection for performing the segmentation through graph cut method. Methods: In the proposed method Fuzzy Kernel Seed Selection technique is used to define the complete brain MRI image into different groups of similar intensity. Among these groups the most accurate kernels are selected empirically that show highest resemblance with the tumor. The concept of fuzziness helps making the selection even at the boundary regions. Results: The proposed Fuzzy kernel selection technique is applied on the BraTS dataset. Among the four modalities, the proposed technique is applied on Flair images. This dataset consists of Low Grade Glioma (LGG) and High Grade Glioma (HGG) tumor images. The experiment is conducted on more than 40 images and validated by evaluating the following performance metrics: 1. Disc Similarity Coefficient (DSC), 2. Jaccard Index (JI) and 3. Positive Predictive Value (PPV). The mean DSC and PPV values obtained for LGG images are 0.89 and 0.87 respectively; and for HGG images it is 0.92 and 0.90 respectively. Conclusion: On comparing the proposed Fuzzy kernel selection graph cut technique approach with the existing techniques it is observed that the former provides an automatic accurate tumor detection. It is highly efficient and can provide a better performance for HGG and LGG tumor segmentation in clinical application.

Automated optimized classification techniques for magnetic resonance brain images

This paper presents automatic tumor detection and classification approaches for brain magnetic resonance images (MRI). These approaches are based on hybrid-optimized classification techniques and classify brain MRI to healthy, benign or malignant. The proposed system implements three-optimization techniques combined with Artificial Neural Network (ANN). Multi-Verse Optimizer (MVO), Moth-Flame Optimizer (MFO) and Salp Swarm Algorithm (SSA) are used and compared to examine how these techniques could be successfully employed to enhance the classification accuracy via selecting the optimal parameters of ANN. The proposed techniques are applied to the Harvard database and BRATS challenge dataset to evaluate the performance via Receiver Operation Characteristics (ROC) analysis. The approaches are tested against geometric transformations such as scaling, rotation and warping to show how much the proposed system resists these transformations. Experimentally, the proposed algorithms achieve the highest classification accuracy as compared to the other published ones. Also, the MVO-ANN algorithm outperforms the other proposed algorithms.

A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images.

As Deep Convolutional Neural Networks (DCNNs) have shown robust performance and results in medical image analysis, a number of deep-learning-based tumor detection methods were developed in recent years. Nowadays, the automatic detection of pancreatic tumors using contrast-enhanced Computed Tomography (CT) is widely applied for the diagnosis and staging of pancreatic cancer. Traditional hand-crafted methods only extract low-level features. Normal convolutional neural networks, however, fail to make full use of effective context information, which causes inferior detection results. In this paper, a novel and efficient pancreatic tumor detection framework aiming at fully exploiting the context information at multiple scales is designed. More specifically, the contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation (DC) Module. A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly. Then, the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions. Finally, the DC Module is specifically designed to capture the interaction information between proposals and surrounding tissues. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately.

Fusion Techniques for the Breast Cancer Detection

Breast cancer is a considerable prime cause which affects the lives of women’s and leads to death all around the world. A tumor is said to be malignancy if a number of cancerous cells can spread to other organs. There has been great impact on the field of medical imaging by the advancement of the computer technology as new and improved techniques of data acquisition, analysis, processing and visualization has evolved. A Digital Breast Tomosynthesis is used for the detection of breast cancer. This is also called as three-dimensional mammography, which eliminates the overlapping tissue problem. Digital Breast Tomosynthesis (DBT) provides information about potential abnormal tissues necessary for medical follow up and diagnosis. DBT gets additional importance in medical science as it is the only preliminary method of diagnosing a breast cancer with the dense breast. Early and accurate diagnosis can be sufficient in resolving various complications and guiding the patient with timely and proper treatment. By considering the above factors there is a great requirement to explore for DBT. The proposed work develops a computer aided methodology for automatic tumor detection and diagnosing in tomosynthesis Patient’s image. This method is incredibly helpful for doctors or the radiologist automatically locates the tumor space within the breast image for further surgery.

Segmentation and Validation of Liver Tumors

Automatic detection, extraction and validation of tumors from the segmented Computed Tomography (CT) liver is a crucial task. Segmentation of tumors provides a landmark to detect and extract tumors from segmented liver image. In this proposed work, Otsu thresholding technique, Level set method, Super pixel-Overlay method and Fast Marching Method (FMM) are used to extract the tumors. Later, Texture features are extracted from the segmented tumors using Gray Level Co-Occurance Matrix (GLCM) and these tumors are validated using Euclidean distance. The work is evaluated on 3DircadB and Clumax dataset using Accuracy, False Positive and Overlap Error parameters. Self relative study and empirical comparative study are performed and results are tabulated. The observation is that Fast Marching method has performed better than existing methods.

On local active contour model for automatic detection of tumor in MRI and mammogram images

Tumor segmentation in medical images is an important step to determine and predict the stage, size and progression of tumors in realistic geometries and may be used for diagnosis and treatment follow-up in MRI and mammogram images. Automating this challenging task helps radiologists to reduce the high manual workload of brain or breast cancer analysis. Detecting tumor in medical images means to quantify the structure contents of the tumor. In this paper, we propose a model for quantifying the structure contents of the tumor in MRI and mammogram images. The proposed model is based on optimized Laplacian of Gaussian, which is useful in smoothing the homogeneous region and highlight the boundaries of the tumor structure. The proposed model uses local intensity information in later stage of segmentation as image data fitting. For this purpose, local Gaussian distribution is used for fitting image data and is combined with level set function. Due to usage of local intensities and level set, the proposed model is able to deal with intensity inhomogeneity and capture different topologies. To stop the leakage of contour at the boundary of tumor, we take some geometrical points near the tumor's boundary and introduce a distance constraint in model. The gradient flow equation and other optimal values are obtained through minimization of the energy functional. The gradient flow equation is solved by using additive operator splitting method. The experimental results of the proposed model are validated by comparing it with existing state of the art models both qualitatively and quantitatively. The proposed model achieves average values of 99.96%, 99.88%, 99.91%, 99.91%, 99.95% for Jaccard similarity, dice coefficient, accuracy, sensitivity and specificity respectively, which are better than the existing models in comparison.

Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

The development of computer aided diagnosis (CAD) systems for the task of tumor detection and segmentation in breast ultrasound (BUS) images presents one of the most active research fields. In this paper, a saliency-guided approach for fast and automatic tumor segmentation in BUS images is proposed. We explore the ability of the concept of saliency to emulate radiologist expertise for tumor lesion detection in BUS images. For that, we compute BUS image saliency to generate effective tumor seeds and background seeds. Then, a graph-based interactive image segmentation method is applied automatically using the generated seeds to extract tumor region. For more accurate segmentation, we propose a novel saliency score measure to apply a post-processing step for result refinement. Our results have been compared with other approaches in challenged datasets and demonstrated the effectiveness of our saliency-guided approach for tumor detection and segmentation using different evaluation metrics.

An Efficient Cascaded CNN Architecture for Brain Tumor Detection in MRI Images

This research work proposed an automated tumor detection system based on cascaded Convolutional Neural Network (CNN) architecture. In this, each input has convolved separately with three kernels (3 x 3, 5 x 5 and 7 x 7) and their three output feature maps are cascaded to be processed into the hierarchy of two convolution and pooling layers followed by fully connected (FC) layer. In FC layer, the softmax classification technique has performed to find the pixel-wise classification and to detect whether the particular image consisting of tumor or not. This proposed work is tested with BRATS-2018 dataset of both Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG) brain images. Further, this work has evaluated using different metrics namely accuracy, precision, recall, F1-score, specificity and sensitivity. Thus, this method outperforms well with 96% accuracy, 98% precision, 98% F1-score and 99% sensitivity, demonstrating that the tumor identification has achieved 5% better accuracy than the existing tumor detection methods

Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning.

Wireless Capsule Endoscopy (WCE) is a minimally invasive diagnosis tool for lesion detection in the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the significant amount of acquired data leads to difficulties in the diagnosis by the physicians; which can be eased with computer assistance. This paper addresses a method for the automatic detection of tumors in WCE by using a two-step based procedure: region of interest selection and classification. The first step aims to separate abnormal from normal tissue by using automatic segmentation based on a Gaussian Mixture Model (GMM). A modified version of the Anderson method for convergence acceleration of the expectation-maximization (EM) algorithm is proposed. The proposed features for both segmentation and classification are based on the CIELab color space, as a way of bypassing lightness variations, where the L component is discarded. Tissue variability among subjects, light inhomogeneities and even intensity differences among different devices can be overcome by using simultaneously features from both regions. In the second step, an ensemble system with partition of the training data with a new training scheme is proposed. At this stage, the gating network is trained after the experts have been trained decoupling the joint maximization of both modules. The partition module is also used at the test step, leading the incoming data to the most likely expert allowing incremental adaptation by preserving data diversity. This algorithm outperforms others based on texture features selected from Wavelets and Curvelets transforms, classified by a regular support vector machine (SVM) in more than 5%. This work shows that simpler features can outperform more elaborate ones if appropriately designed. In the current case, luminance was discarded to cope with saturated tissue, facilitating the color perception. Ensemble systems remain an open research field. In the current case, changes in both topology and training strategy have led to significant performance improvements. A system with this level of performance can be used in current clinical practice.

Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region‐growing and quasi‐Monte Carlo‐expectation maximization algorithm

AbstractMagnetic resonance imaging (MRI) is widely used in the medical field, especially for detecting serious abnormalities affecting the organs of the human body, such as tumors. Automatic detection of tumors needs high‐performance recognition techniques. In this paper, we have developed a new automatic method based on the multisegmentation of brain tumor region. We used an improved region‐growing algorithm, which is based on quasi‐Monte Carlo and expectation maximization methods to define the desired classes. Several metrics were calculated to evaluate the performance of our technique. The fully automatic multisegmentation approach, developed in this study, showed good performance, and it can offer a new option to replace conventional techniques used for tumor detection in MRI images.

APSO based LLRBFNN Model and Enhanced Fuzzy C Means algorithm for Brain Tumor Detection and Classification from Magnetic Resonance Image

This paper presents a novel APSO (Accelerated Particle Swarm Optimization) Predicated LLRBFNN (Local Linear Radial Basis Function Neural Network) model for automatic encephalon tumor detection and classification. The enhanced fuzzy c means algorithm (EnFCM) has been proposed for image segmentation and the GLCM (Gray Level Co-occurrence Matrix) technique for feature extraction from MR (Magnetic Resonance) images. This research work aims to utilize the hybrid models and algorithms for relegation and segmentation of encephalon tumors from the MR images. The extracted features have been alimented as input to the proposed APSO predicated LLRBFNN model for relegation of benign and malignant tumors. In this research work the proposed LLRBFNN model weights are optimized by utilizing APSO training which will provide unique solution to mitigation the hectic task of radiologist from manual detection of encephalon tumors from MR Images. Additionally the centers of the LLRBFNN model are culled by the Enhanced Fuzzy C Means algorithm and updated by the APSO algorithm. The results of proposed PSO predicated LLRBFNN model has been compared with PSO-LLRBFNN model, APSO-RBFNN and PSO-RBFNN model and the comparison results are presented. The experimental results obtained from the proposed model shows better relegation results as compared to the subsisting models proposed anteriorly.

Salient object detection method for breast tumor in ultrasound images based on absorbing Markov chain.

Automatic detection of tumor in breast ultrasound (BUS) images is important for the subsequent image processing and has been researched for decades. However, there still lacks a robust method due to poor quality of BUS images. To propose and test a salient object detection method for BUS images. BUS image is preprocessed by an adaptively selective replacement and speckle reducing anisotropic diffusion (SRAD) algorithm. Then, the preprocessed image is segmented into super pixels by a simple linear iterative clustering (SLIC) algorithm to form a graph model, and the saliency of the nodes in the graph is calculated by using the absorbed time of absorbing Markov chain (AMC). Finally, the initial saliency map is optimized by the recurrent time of ergodic Markov chain (EMC) and a distance weighting formula. Results of the proposed method were compared both qualitatively and quantitatively with two saliency detection models. It was observed that the proposed method outperformed the comparison models and yielded the highest Accuracy value (97.49% vs. 86.63% and 90.33%) using a dataset of 1000 BUS images. After the adaptively selective replacement, AMC can effectively distinguish tumors from background by random walks.

Brain Tumor Detection and Segmentation by Intensity Adjustment.

In recent years, Brain tumor detection and segmentation has created an interest on research areas. The process of identifying and segmenting brain tumor is a very tedious and time consuming task, since human physique has anatomical structure naturally. Magnetic Resonance Image (MRI) scan analysis is a powerful tool that makes effective detection of the abnormal tissues from the brain. Among different techniques, Magnetic Resonance Image (MRI) is a liable one which contains several modalities in scanning the images captured from interior structure of human brain. A novel hybrid energy-efficient method is proposed for automatic tumor detection and segmentation. The proposed system follows K-means clustering, integrated with Fuzzy C-Means (KMFCM) and active contour by level set for tumor segmentation. An effective segmentation, edge detection and intensity enhancement can detect brain tumor easily. For that, active contour with level set method has been utilized. The performance of the proposed approach has been evaluated in terms of white pixels, black pixels, tumor detected area, and the processing time. This technique can deal with a higher number of segmentation problem and minimum execution time by ensuring segmentation quality. Additionally, tumor area length in vertical and horizontal positions is determined to measure sensitivity, specificity, accuracy, and similarity index values. Further, tumor volume is computed. Knowledge of the information of tumor is helpful for the physicians for effective diagnosing in tumor for treatments. The entire experimentation was implemented in MATLAB environment and simulation results were compared with existing approaches.

Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.

A New Discrete Wavelet Transform Algorithm Based on Frame Theory and Its Application to Brain MRI Segmentation

Automated brain tumor detection from MRI images is a very challenging job in today's modern medical imaging research. MRI is used to take image of soft tissues of human body. It is very helpful for analyzing human organs without surgical intervention. For automatic detection of tumor, segmentation of brain image is required. Segmentation partitions the image into distinct regions based on various parameters. It is the most important and challenging area of computer aided clinical diagnostic tools. Although Conventional segmentation approaches are computationally efficient, but have low quality of edge and feature detection. Here, we propose an algorithm on frame theoretic methods and the discrete wavelet transform and apply it to brain MRIs. Significant gains in performance are observed over conventional segmentation algorithms.

Pathophysiological mapping of tumor habitats in the breast in DCE-MRI using molecular texture descriptor

BackgroundWe propose a computational methodology capable of detecting and analyzing breast tumor habitats in images acquired by magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI), based on the pathophysiological behavior of the contrast agent (CA). MethodsThe proposed methodology comprises three steps. In summary, the first step is the acquisition of images from the Quantitative Imaging Network Breast. In the second step, the segmentation of the breasts is performed to remove the background, noise, and other unwanted objects from the image. In the third step, the generation of habitats is performed by applying two techniques: the molecular texture descriptor (MTD) that highlights the CA regions in the breast, and pathophysiological texture mapping (MPT), which generates tumor habitats based on the behavior of the CA. The combined use of these two techniques allows the automatic detection of tumors in the breast and analysis of each separate habitat with respect to their malignancy type. ResultsThe results found in this study were promising, with 100% of breast tumors being identified. The segmentation results exhibited an accuracy of 99.95%, sensitivity of 71.07%, specificity of 99.98%, and volumetric similarity of 77.75%. Moreover, we were able to classify the malignancy of the tumors, with 6 classified as malignant type III (WashOut) and 14 as malignant type II (Plateau), for a total of 20 cases. ConclusionWe proposed a method for the automatic detection of tumors in the breast in DCE-MRI and performed the pathophysiological mapping of tumor habitats by analyzing the behavior of the CA, combining MTD and MPT, which allowed the mapping of internal tumor habitats.

Efficient Segmentation of Brain Tumor Using FL-SNM with a Metaheuristic Approach to Optimization.

Nowadays, automatic tumor detection from brain images is extremely significant for many diagnostic as well as therapeutic purposes, due to the unpredictable shape and appearance of tumors. In medical image analysis, the automatic segmentation of tumors from brain using magnetic resonance imaging (MRI) data is the most critical issue. Existing research has some limitations, such as high processing time and lower accuracy, because of the time required for the training process. In this research, a new automatic segmentation process is introduced using machine learning and a swarm intelligence scheme. Here, a fuzzy logic with spiking neuron model (FL-SNM) is proposed for segmenting the brain tumor region in MR images. Initially, input images are preprocessed to remove Gaussian and Poisson noise using a modified Kuan filter (MKF). In the MKF, the optimal selection of the minimum MSE of image pixels is achieved using a random search algorithm (RSA), which improves the peak signal-to-noise ratio (PSNR). Then, the image is smoothed using an anisotropic diffusion filter (ADF) to reduce the over-filtering problem. Afterwards, to extract statistical texture features, Fisher's linear-discriminant analysis (FLDA) is used. Finally, extracted features are transferred to the FL-SNM process and this scheme effectively segments the tumor region. In FL-SNM, the consequent parameters such as weight and bias play an important role in segmenting the region. Therefore, optimizing the weight parameter values using a chicken behavior-based swarm intelligence (CSI) algorithm, is proposed. The proposed (FL-SNM) scheme attained better performance in terms of high accuracy (94.87%), sensitivity (92.07%), specificity (99.34%), precision rate (89.36%), recall rate (88.39%), F-measure (95.06%), G-mean (95.63%), and DSC rate (91.2%), compared to existing convolutional neural networks (CNNs) and hierarchical self-organizing maps (HSOMs).