Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radius fractures (DRFs) on anterior-posterior (AP) wrist radiographs. Patients and methods - 2,340 AP wrist radiographs from 2,340 patients were enrolled in this study. We trained the CNN to analyze wrist radiographs in the dataset. Feasibility of the object detection algorithm was evaluated by intersection of the union (IOU). The diagnostic performance of the network was measured by area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity, and Youden Index; the results were compared with those of medical professional groups. Results - The object detection model achieved a high average IOU, and none of the IOUs had a value less than 0.5. The AUC of the CNN for this test was 0.96. The network had better performance in distinguishing images with DRFs from normal images compared with a group of radiologists in terms of the accuracy, sensitivity, specificity, and Youden Index. The network presented a similar diagnostic performance to that of the orthopedists in terms of these variables. Interpretation - The network exhibited a diagnostic ability similar to that of the orthopedists and a performance superior to that of the radiologists in distinguishing AP wrist radiographs with DRFs from normal images under limited conditions. Further studies are required to determine the feasibility of applying our method as an auxiliary in clinical practice under extended conditions.

https://www.tandfonline.com/doi/pdf/10.1080/17453674.2019.1600125

Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments



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.



 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.



 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.



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.


Brain Tumor Detection from MR Images Employing Fuzzy Graph Cut Technique

Enhanced salp swarm algorithm based on firefly algorithm for unrelated parallel machine scheduling with setup times

In computer vision, traditional machine learning (TML) and deep learning (DL) methods have significantly contributed to the advancements of medical image analysis (MIA) by enhancing prediction accuracy, leading to appropriate planning and diagnosis. These methods substantially improved the diagnoses of automatic brain tumor and leukemia/blood cancer detection and can assist the hematologist and doctors by providing a second opinion. This review provides an in-depth analysis of available TML and DL techniques for MIA with a significant focus on leukocytes classification in blood smear images and other medical imaging domains, i.e., magnetic resonance imaging (MRI), CT images, X-ray, and ultrasounds. The proposed review’s main impact is to find the most suitable TML and DL techniques in MIA, especially for leukocyte classification in blood smear images. The advanced DL techniques, particularly the evolving convolutional neural networks-based models in the MIA domain, are deeply investigated in this review article. The related literature study reveals that mainstream TML methods are vastly applied to microscopic blood smear images for white blood cells (WBC) analysis. They provide valuable information to medical specialists and help diagnose various hematic diseases such as AIDS and blood cancer (Leukaemia). Based on WBC related literature study and its extensive analysis presented in this study, we derive future research directions for scientists and practitioners working in the MIA domain.

/pdf/a-review-on-traditional-machine-learning-and-deep-learning-4d74zo8iit.pdf

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Computer-aided diagnosis of liver lesions using CT images: A systematic review.

Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.

Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.

The role of compression is vital in telemedicine for the storage and transmission of medical images. This work is based on Contextual Vector Quantization (CVQ) compression algorithm with codebook optimization by Simulated Annealing (SA) for the compression of CT images. The region of interest (foreground) and background are separated initially by region growing algorithm. The region of interest is encoded with low compression ratio and high bit rate; the background region is encoded with high compression ratio and low bit rate. The codebook generated from foreground and background is merged, optimized by simulated annealing algorithm. The performance of CVQ-SA algorithm was validated in terms of metrics like Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR), the result was superior when compared with classical VQ, CVQ, JPEG lossless and JPEG lossy algorithms. The algorithms are developed in Matlab 2010a and tested on real-time abdomen CT datasets. The quality of reconstructed image was also validated by metrics like Structural Content (SC), Normalized Absolute Error (NAE), Normalized Cross Correlation (NCC) and statistical analysis was performed by Mann Whitney U Test. The outcome of this work will be an aid in the field of telemedicine for the transfer of medical images.

Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application

Image segmentation plays a vital role in medical image processing for the delineation of anatomical organs and analysis of anomalies. The evaluation of segmentation algorithms is vital to select the appropriate algorithm and parameters for optimum performance. In this paper, we are describing various metrics for evaluating the quality of segmentation algorithms with respect to ground truth images. The analysis of metrics has been carried out on real-time data sets of abdomen and retina. The variants of active contour algorithms are employed for the abdomen CT images, Kirsch and Wavelet algorithm were used for the retinal fundus images. This paper presents performance evaluation parameters that can be used to analyze efficiency of segmentation algorithms.

Performance Metric Evaluation of Segmentation Algorithms for Gold Standard Medical Images

The segmentation is the process of extraction of the desired region of interest. In medical images, the anatomical organs and anomalies like a tumor, cysts, etc. are of importance for the diagnosis of diseases by physicians for telemedicine applications. The thresholding, region growing, and edge detection are termed as classical segmentation algorithms. Clustering is an unsupervised learning technique to group similar data points and fuzzy partitioning merges similar pixels based on the fuzzy membership value. The classical FCM algorithm lacks sensitivity in the cluster centroid initialization and often gets trapped in local minima. The optimization algorithm gains its importance in cluster centroids initialization, thereby improving the efficiency of FCM algorithm. In this work, firefly optimization is coupled with FCM algorithm for CT/MR medical image segmentation. Fireflies are insects having a natural capacity to illumine in dark with glowing and flickering lights and firefly optimization algorithm was modeled based on its biological traits. The preprocessing stage comprises of artifacts removal and denoising by Nonlinear Tensor Diffusion (NLTD) filter. The computation time was minimized by reducing the total pixels count for the processing. The Firefly optimization, when coupled with FCM, generates satisfactory results inconsistent with FCM when coupled with Cuckoo, Artificial Bee Colony, and Simulated annealing algorithms. The cluster validity performance metrics are used for the determination of optimum number of clusters. The algorithms are developed in Matlab 2010a and tested on real-time abdomen datasets.

Firefly Optimization Based Improved Fuzzy Clustering for CT/MR Image Segmentation

The storage and transmission of medical data such as CT/MR DICOM images are an essential part of the telemedicine application. In this paper, a prediction-based lossless compression algorithm using least square approach is proposed for the compression of CT images. Prior to compression, the preprocessing was performed by neutrosophic median filter. The gradient adjusted prediction scheme was employed for the determination of prediction coefficients, and polynomial least square fitting approach was used for optimal selection of prediction coefficients. The selected prediction coefficients are finally encoded by Huffman coder for transmission. The quality of the reconstructed image was validated by performance metrics and compared with other compression techniques like JPEG, contextual vector quantization and vector quantization using bat optimization (BAT-VQ). The proposed neutrosophic set-based least square compression algorithm was found to be efficient and tested on DICOM abdomen CT datasets. The hardware implementation was done by Raspberry Pi processor using Java platform for transferring the data through cloud network for telemedicine application.

P. Sebastin Varghese

Papers

Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.

Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application

Performance Metric Evaluation of Segmentation Algorithms for Gold Standard Medical Images

Firefly Optimization Based Improved Fuzzy Clustering for CT/MR Image Segmentation

Lossless Compression of CT Images by an Improved Prediction Scheme Using Least Square Algorithm