Tumors In Medical Images Research Articles

ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process

Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filter helps to smooth out any irregularities in the images, while the image scaling method adjusts the size and resolution of the images for better analysis. Once the preprocessing is complete, the preprocessed image is given to the segmentation phase. The segmentation phase is crucial in medical image analysis as it helps to identify and separate different structures within the image, such as organs or tumors. This process involves dividing the preprocessed image into meaningful regions or segments based on intensity, color, texture, or other features. The segmentation process is done using Adaptive Thresholding with Region Growing Fusion Model (AT-RGFM)”. This model combines the advantages of both thresholding and region-growing techniques to accurately identify and delineate specific structures within the image. By utilizing AT-RGFM, the segmentation phase can effectively differentiate between different parts of the image, allowing for more precise analysis and diagnosis. It plays a vital role in the medical image analysis process, providing crucial insights for healthcare professionals. Here, the Modified Garter Snake Optimization Algorithm (MGSOA) is used to optimize the parameters. It helps to optimize parameters for accurately identifying and delineating specific structures within medical images and also helps healthcare professionals in providing more precise analysis and diagnosis, ultimately playing a vital role in the medical image analysis process. MGSOA enhances the segmentation phase by effectively differentiating between different parts of the image, leading to more accurate results. Then, the segmented image is fed into the detection phase. The tumor detection is performed by the Vision Transformer-based Multiscale Adaptive EfficientNetB7 (ViT-MAENB7) model. This model utilizes a combination of advanced algorithms and deep learning techniques to accurately identify and locate tumors within the segmented medical image. By incorporating a multiscale adaptive approach, the ViT-MAENB7 model can analyze the image at various levels of detail, improving the overall accuracy of tumor detection. This crucial step in the medical image analysis process allows healthcare professionals to make more informed decisions regarding patient treatment and care. Here, the created MGSOA algorithm is used to optimize the parameters for enhancing the performance of the model. The suggested breast cancer diagnosis performance is compared to conventional cancer diagnosis models and it showed high accuracy. The accuracy of the developed MGSOA-ViT-MAENB7 is 96.6 %, and others model like RNN, LSTM, EffNet, and ViT-MAENet given the accuracy to be 90.31 %, 92.79 %, 94.46 % and 94.75 %. The developed model's ability to analyze images at multiple scales, combined with the optimization provided by the MGSOA algorithm, results in a highly accurate and efficient system for detecting tumors in medical images. This cutting-edge technology not only improves the accuracy of diagnosis but also helps healthcare professionals tailor treatment plans to individual patients, ultimately leading to better outcomes. By outperforming traditional cancer diagnosis models, the proposed model is revolutionizing the field of medical imaging and setting a new standard for precision and effectiveness in healthcare.

A Dynamic Context Encoder Network for Liver Tumor Segmentation.

Accurate segmentation of liver tumor regions in medical images is of great significance for clinical diagnosis and the planning of surgical treatments. Recent advancements in machine learning have shown that convolutional neural networks are powerful in such image processing while largely reducing human labor. However, the variable shape, fuzzy boundary, and discontinuous tumor region of liver tumors in medical images bring great challenges to accurate segmentation. The feature extraction capability of a neural network can be improved by expanding its architecture, but it inevitably demands more computing resources in training and hyperparameter tuning. This study presents a Dynamic Context Encoder Network (DCE-Net), which incorporates multiple new modules, such as the Involution Layer, Dynamic Residual Module, Context Extraction Module, and Channel Attention Gates, for feature extraction and enhancement. In the experiment, we used a liver tumor CT dataset of LiTS2017 to train and test the DCE-Net for liver tumor segmentation. The experimental results showed that the four evaluation indexes of the method, precision, recall, dice, and AUC, were 0.8961, 0.9711, 0.9270, and 0.9875, respectively. Furthermore, our ablation study reported that the accuracy and training efficiency of our network were markedly superior to the networks without involution or dynamic residual modules. Therefore, the DCE-Net proposed in this study has great potential for automatic segmentation of liver lesion tumors in the clinical diagnostic environment.

Detecting tumors in medical images using segmentation and feature extraction techniques

Tumors rank among the most prevalent and lethal diseases worldwide, emphasizing the paramount importance of early detection in mitigating mortality rates. There has been a tremendous growth in medical image segmentation and classification for diagnosing cancer. This led to the various developments in medical research that help in diagnosing types of cancers and other human medical problems. This work focuses on performing effective segmentation procedures and feature extraction by a proposed hybrid intelligent technique for detecting tumors in the medical images. Additionally, the research addresses the design aspects of feature extraction, image classification, and texture features. These features are reduced by employing feature selection. Finally, experiments were conducted to compare tumor detection in medical images using the proposed algorithm with other existing algorithms. The results demonstrated promising achievements in terms of their evaluation metrics.

3D Multi-Organ and Tumor Segmentation Based on Re-Parameterize Diverse Experts

Automated segmentation of abdominal organs and tumors in medical images is a challenging yet essential task in medical image analysis. Deep learning has shown excellent performance in many medical image segmentation tasks, but most prior efforts were fragmented, addressing individual organ and tumor segmentation tasks with specialized networks. To tackle the challenges of abdominal organ and tumor segmentation using partially labeled datasets, we introduce Re-parameterizing Mixture-of-Diverse-Experts (RepMode) to abdominal organ and tumor segmentation. Within the RepMode framework, the Mixture-of-Diverse-Experts (MoDE) block forms the foundation, learning generalized parameters applicable across all tasks. We seamlessly integrate the MoDE block into a U-shaped network with dynamic heads, addressing multi-scale challenges by dynamically combining experts with varying receptive fields for each organ and tumor. Our framework incorporates task encoding in both the encoder–decoder section and the segmentation head, enabling the network to adapt throughout the entire system based on task-related information. We evaluate our approach on the multi-organ and tumor segmentation (MOTS) dataset. Experiments show that DoDRepNet outperforms previous methods, including multi-head networks and single-network approaches, giving a highly competitive performance compared with the original single network with dynamic heads. DoDRepNet offers a promising approach to address the complexities of abdominal organ and tumor segmentation using partially labeled datasets, enhancing segmentation accuracy and robustness.

The Role of Machine Learning in the Detection and Classification of Brain Tumors: A Literature Review of the Past Two Years

A brain tumor is an abnormal growth of cells in the brain. There are four common types of brain tumors.  Doctors can segment and identify the tumors manually, but it is very time-consuming. There exist automatic segmentation algorithms that can facilitate the process. Deep learning is a new method of creating powerful AI models. As a result, there is a need for automatic segmentation algorithms that can facilitate the process and improve the accuracy of brain tumor detection. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for developing such algorithms. In particular, deep learning (DL) methods, such as convolutional neural networks (CNNs), have shown great potential for accurately identifying brain tumors in medical images. This paper presents a literature review of recently published papers (2020-2022) on brain tumor classification and detection using artificial intelligence. The review covers various AI and DL methods, including supervised learning, reinforcement learning, and unsupervised learning. It evaluates their effectiveness in detecting and classifying brain tumors in medical images. The review also discusses the challenges and limitations of these methods, as well as future directions for research in this field.

Auto-segmentation of head and neck tumors in positron emission tomography images using non-local means and morphological frameworks.

Accurately segmenting head and neck cancer (HNC) tumors in medical images is crucial for effective treatment planning. However, current methods for HNC segmentation are limited in their accuracy and efficiency. The present study aimed to design a model for segmenting HNC tumors in three-dimensional (3D) positron emission tomography (PET) images using Non-Local Means (NLM) and morphological operations. The proposed model was tested using data from the HECKTOR challenge public dataset, which included 408 patient images with HNC tumors. NLM was utilized for image noise reduction and preservation of critical image information. Following pre-processing, morphological operations were used to assess the similarity of intensity and edge information within the images. The Dice score, Intersection Over Union (IoU), and accuracy were used to evaluate the manual and predicted segmentation results. The proposed model achieved an average Dice score of 81.47 ± 3.15, IoU of 80 ± 4.5, and accuracy of 94.03 ± 4.44, demonstrating its effectiveness in segmenting HNC tumors in PET images. The proposed algorithm provides the capability to produce patient-specific tumor segmentation without manual interaction, addressing the limitations of current methods for HNC segmentation. The model has the potential to improve treatment planning and aid in the development of personalized medicine. Additionally, this model can be extended to effectively segment other organs from limited annotated medical images.

Hardware approach to brain tumour detection using AI concepts using real time embedded systems with Raspberry pi

Tumors have become the second leading cause of cancer nowadays, posing significant risks to numerous patients. The medical field is in urgent need of rapid, automated, efficient, and reliable techniques to detect tumors, especially brain tumors. Early and accurate detection plays a crucial role in successful treatment and keeping patients safe. To address this challenge, various image processing techniques are employed in the medical domain. These advancements have allowed doctors to administer appropriate treatments, leading to the successful recovery of many tumor patients. Tumors are characterized by the abnormal growth of cells, which proliferate uncontrollably. Brain tumors, in particular, can be devastating as they compete with healthy cells and tissues for essential nutrients, ultimately resulting in brain dysfunction. Traditionally, doctors have relied on manual examination of MR images to identify the location and extent of brain tumors. Unfortunately, this approach is prone to errors and can be extremely time-consuming. To overcome these limitations, we have implemented a cutting-edge deep learning architecture known as Convolution Neural Network (CNN), a type of Neural Network (NN) that utilizes Transfer Learning. This CNN-based model enables us to automatically detect the presence of brain tumors in medical images with high accuracy. If a tumor is detected, the model outputs a positive result; otherwise, it indicates the absence of a tumor. In our approach, we use K-means clustering in conjunction with Raspberry Pi to precisely pinpoint the location of the brain tumor. This ensures targeted and efficient treatment planning. Additionally, we have integrated an Arduino controller to facilitate the movement of robotic wheels, allowing for precise navigation to the exact location of the tumor during medical interventions. Overall, our system represents a significant advancement in tumor detection and localization, offering faster and more reliable results compared to manual methods. By leveraging state-of-the-art technology and innovative techniques, we strive to enhance patient outcomes, ultimately saving more lives in the fight against brain tumors.

A Brain Tumor Image Segmentation Method Based on Quantum Entanglement and Wormhole Behaved Particle Swarm Optimization.

PurposeAlthough classical techniques for image segmentation may work well for some images, they may perform poorly or not work at all for others. It often depends on the properties of the particular image segmentation task under study. The reliable segmentation of brain tumors in medical images represents a particularly challenging and essential task. For example, some brain tumors may exhibit complex so-called “bottle-neck” shapes which are essentially circles with long indistinct tapering tails, known as a “dual tail.” Such challenging conditions may not be readily segmented, particularly in the extended tail region or around the so-called “bottle-neck” area. In those cases, existing image segmentation techniques often fail to work well.MethodsExisting research on image segmentation using wormhole and entangle theory is first analyzed. Next, a random positioning search method that uses a quantum-behaved particle swarm optimization (QPSO) approach is improved by using a hyperbolic wormhole path measure for seeding and linking particles. Finally, our novel quantum and wormhole-behaved particle swarm optimization (QWPSO) is proposed.ResultsExperimental results show that our QWPSO algorithm can better cluster complex “dual tail” regions into groupings with greater adaptability than conventional QPSO. Experimental work also improves operational efficiency and segmentation accuracy compared with current competing reference methods.ConclusionOur QWPSO method appears extremely promising for isolating smeared/indistinct regions of complex shape typical of medical image segmentation tasks. The technique is especially advantageous for segmentation in the so-called “bottle-neck” and “dual tail”-shaped regions appearing in brain tumor images.

Brain Tumors Detection By Using Convolutional Neural Networks and Selection of Thresholds By Histogram Selection

Brain tumors in medical images have a high diversity in terms of shape and size. Some of the data found a form between the tumor tissue and normal tissue, whereas knowing the tumor’s profile and characteristics becomes a crucial part of searching. By using machine learning capabilities, where machines are given several variables and provide decisions to a certain degree, they have broadly given decisions that support subject matter in making decisions. This study applies the threshold selection method using histogram selection on CT scan data, while the appropriate threshold selection method selects the tumor position accordingly. Furthermore, the Convolutional Neural Network (CNN) is used to classify whether the selected image is a tumor or not. Using CT scan data and calculated experiments, this algorithm can finally be approved and given a brain classification with an accuracy of 75.42 percent.

A Deep-Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation

The segmentation of brain tumors in medical images is a crucial step of clinical treatment. Manual segmentation is time consuming and labor intensive, and existing automatic segmentation methods suffer from issues such as numerous parameters and low precision. To resolve these issues, this study proposes a learnable group convolution-based segmentation method that replaces convolution in the feature extraction stage with learnable group convolution, thereby reducing the number of convolutional network parameters and enhancing communication between convolution groups. To improve utilization of the feature maps, we added a skip connection structure between learnable group convolution modules, which increased segmentation precision. We used deep supervision to combine output images in the network output stage to reduce overfitting and enhance the recognition capabilities of the network. We tested the proposed algorithm model using the open BraTS 2018 dataset. The experiment results revealed that the proposed model is superior to 3D U-Net and DMFNet and has better segmentation results for tumor cores than No New-Net and NVDLMED, the winning methods in the BraTS 2018 challenge. The segmentation precision of the proposed method with regard to whole tumors, enhancing tumors, and tumor cores was 90.25%, 80.36%, and 86.20%. Furthermore, the proposed method uses fewer parameters and a less complex model.

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)

Benchmarking Human Performance in Semi-Automated Image Segmentation

AbstractSemi-automated segmentation algorithms hold promise for improving extraction and identification of objects in images such as tumors in medical images of human tissue, counting plants or flowers for crop yield prediction or other tasks where object numbers and appearance vary from image to image. By blending markup from human annotators to algorithmic classifiers, the accuracy and reproducability of image segmentation can be raised to very high levels. At least, that is the promise of this approach, but the reality is less than clear. In this paper, we review the state-of-the-art in semi-automated image segmentation performance assessment and demonstrate it to be lacking the level of experimental rigour needed to ensure that claims about algorithm accuracy and reproducability can be considered valid. We follow this review with two experiments that vary the type of markup that annotators make on images, either points or strokes, in tightly controlled experimental conditions in order to investigate the effect that this one particular source of variation has on the accuracy of these types of systems. In both experiments, we found that accuracy substantially increases when participants use a stroke-based interaction. In light of these results, the validity of claims about algorithm performance are brought into sharp focus, and we reflect on the need for a far more control on variables for benchmarking the impact of annotators and their context on these types of systems.

Integration of a knowledge-based constraint into generative models with applications in semi-automatic segmentation of liver tumors

Accurate delineation of liver tumors in medical images is a vital step in diagnosis, treatment planning, and monitoring. In this paper, we utilize a generative model for segmentation of abnormal liver regions. After preprocessing of an input image, the ROI of the tumor is determined, and the boundary of the abnormal region in a single slice is specified. Then, the remaining slices are processed by a generative model that is enhanced by the integration of a constraint. We search for the boundary of the tumor by a probabilistic approach and obtain the solution using the Bayesian inference. The Kullback-Leibler divergence is used to measure the consistency of the results to the model’s constraint. We evaluated the proposed method using synthetic and clinical data. In the public dataset, we achieved a Dice measure of 0.84 ± 0.06, which outperforms state-of-the-art hepatic tumor segmentation algorithms. Concerning all available clinical images, the Dice index of the proposed method is 0.90 ± 0.03.

An Effective System for Content MRI Brain Image Retrieval using Angular Radial Transform

Nowadays, the growth of huge amount of medical image has become one of the most important clinical diagnosis components, Furthermore, there is an urgent need a system of Content Based Image Retrieval (CBIR) to obtain essential information such as type of image and extracting features of the image, such as color, shape and texture. This system is based on the image content that retrieves similar pathology involving magnetic resonance (MR) images of the medical database to assist the radiologist in the diagnosis of brain tumor; the shape recovery is the one of the top performers. Content-based image retrieval can also be used to locate brain tumors in medical images in large databases. In this paper, we propose a new method to build a new research system by the content of MRI images (CBIR) based on image characteristics, such as shape using the ART descriptor (angular radial transform) that is applied to reveal the characteristics of MR images. ART descriptor (angular radial transform) of shape is based Region adopted in MPEG-7 has the properties invariant to scale, rotation and robustness to noise, thanks to its properties we have used in this system. After the segmentation process, extracting the visual features of shape by calculating the coefficients of the ART and forming a second feature vector to be input to a support vector machine (SVM) for determining the presence tumor or not tumor followed by KNN (K-nearest neighbor) that retrieves the most similar images in the database. To provide faster image search. General Terms Content based image retrieval; Classification;

Segmentation of Tumors from Ultrasound Images with PAORGB

Abstract Segmentation is an unavoidable process for computer-aided diagnosis in medical image processing. Due to complex artifacts accurate extraction of tumors in medical images remains a challenge. The existing system deals with the manual cut of the tumor centered image along with robust graph based segmentation for accurate extraction of tumor. The input image can be obtained from any type of modality. There are different ways to segment breast tumor images. This paper focuses on a novel technique called parameter automatically optimized robust graph based (PAORGB) segmentation. Particle swam optimization is incorporated with robust graph based segmentation in order to obtain a global solution. The new method may be a effective way to segment breast tumors than the robust graph based method and to also reduce the computational time.

Extraction and 3D Segmentation of Tumors-Based Unsupervised Clustering Techniques in Medical Images

Introduction The diagnosis and separation of cancerous tumors in medical images require accuracy, experience, and time, and it has always posed itself as a major challenge to the radiologists and physicians. Materials and Methods We Received 290 medical images composed of 120 mammographic images, LJPEG format, scanned in gray-scale with 50 microns size, 110 MRI images including of T1-Wighted, T2-Wighted, and Proton Density (PD) images with 1-mm slice thickness, 3% noise and 20% intensity non-uniformity (INU) as well as 60 lung cancer images acquired using the 3D CT scanner, GE Medical System LightSpeed QX/i helical, yielding 16-bit slices taken from various medical databases. By applying the Discrete Wavelet Transform (DWT) on the input images and constructing the approximate coefficients of scaling components, the different parts of image were classified. In next step using k-means algorithm, the appropriate threshold was selected and finally the suspicious cancerous mass was separated by implementation image processing techniques. Results By implementing the proposed algorithm, acceptable levels of accuracy 92.06%, sensitivity 89.42%, and specificity 93.54% were resulted for separating the target area from the rest of image. The Kappa coefficient was approximately 0.82 which illustrate suitable reliability for system performance. The correlation coefficient of physician’s early detection with our system was highly significant (p<0.05). Conclusion The precise positioning of the cancerous tumor enables the radiologists to determine the progress level of the disease. The low Positive Predictive Value (PPV) and high Negative Predictive Value (NPV) of the system is a warranty of the system and both clinical specialist and patients can trust the software and output.

Semi-automatic Segmentation of Brain Tumors Using Population and Individual Information

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

Brain Tumor Detection using Fuzzy Support Vector Machine Classification based on a Texton Co-occurrence Matrix

Segmentation of tumors in medical images is not only of high interest in serial treatment monitoring of &#x201C;disease burden&#x201D; in oncologic imaging, but is also gaining popularity with the advance of image guided surgical approaches. Magnetic resonance images are widely used in the diagnosis of brain tumors. In this article, an automatic tumor detection and classification system is presented, which focuses on the structural study on both tumorous and normal tissue. The proposed system consists of the following steps: (i) pre-processing, (ii) feature extraction using an enhanced texton co-occurrence matrix and (iii) classification. In classification, a fuzzy logic based support vector machine is used to classify the experimental images into normal and abnormal. The obtained experimental results show that the proposed brain tumor detection approach is more robust than other neural network based classifiers, feed forward neural network and radial basis function, in terms of sensitivity, specificity and accuracy.

DoctorEye: A Clinically Driven Multifunctional Platform, for Accurate Processing of Tumors in Medical Images

This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results.AvailabilityThe platform, a manual and tutorial videos are available at: http://biomodeling.ics.forth.gr.It is free to use under the GNU General Public License.

DoctorEye: A Clinically Driven Multifunctional Platform, for Accurate Processing of Tumors in Medical Images~!2009-12-08~!2010-03-04~!2010-07-21~!

DoctorEye: A Clinically Driven Multifunctional Platform, for Accurate Processing of Tumors in Medical Images~!2009-12-08~!2010-03-04~!2010-07-21~!