Edge detection of medical image processing using vector field analysis

Ultrasound (US) imaging is considered as one of the most advanced diagnostic tools in medical use. However, a drawback of the medical US imaging is its poor quality of the image, which is affected by speckle noise. In this paper, a MultiLayer Back-Propagation Neural Networks (MLBPNNs) with the Epanechnikov fuzzy function is proposed to reduce the speckle, and while at the same time, enhance the lesion boundaries of the US image. The main goal of the proposed method is to improve the quality of US image so as to improve the quality of the humans interpretation and the computer systems autoedge detection. In order to automatically detect the lesion boundary by a computer system, an edge enhancement is required. Evaluating the simulation results by Peak Signal to Noise Ratio (PSNR), Normalized Mean Square Error (NMSE), Detail Variance (DV), and Background Variance (BV), the proposed method demonstrates an increased performance of reducing the speckle and enhancing the edge. The proposed method has higher PSNR than conventional methods and can remove the speckle sufficiently, so that tumor boundaries of real US breast tumor image could be preserved and detected.

Computer Aided Segmentation of Breast Ultrasound Images Using Scale Invariant Feature Transform (SIFT) and Bag Of Features

The segmentation and classification of breast ultrasound (BUS) images are crucial for the early diagnosis of breast cancer and remain a key focus in BUS image processing. Numerous machine learning and deep learning algorithms have shown their effectiveness in the segmentation and diagnosis of BUS images. In this work, we propose a multi-task learning network with an object contextual attention module (MTL-OCA) for the segmentation and classification of BUS images. The proposed method utilizes the object contextual attention module to capture pixel-region relationships, enhancing the quality of segmentation masks. For classification, the model leverages high-level features extracted from unenhanced segmentation masks to improve accuracy. Cross-validation on a public BUS dataset demonstrates that MTL-OCA outperforms several current state-of-the-art methods, achieving superior results in both classification and segmentation tasks.

In this paper ,a new method for edge detection in multispectral imags is presented. It is based on the use of the Self-Organizing Map (SOM), Peano scan and a conventional edge detector. The method presented in this paper order the vectors of the original image in such a way that vectors that are near each other according to some similarity criterium have scalar ordering values near each other. This is achieved using a 2D self-organizing map and the Peano scan. After ordering, the original vector image reduces to a gray-value image, and a conventional edge detector can be applied. In this paper, the Laplace and the Canny edge detectors are used. It is shown, that using the proposed method sit is possible to find the same relevant edges that R-ordering based methods find. Furthermore, it is also possible to find edges in images which consist of metameric colors, i.e. images in which every pixel vector maps into the same location in RGB space. This is not possible using conventional edge detectors which use an RGB image as input. Finally, the new method is tested with a real-world airplane image, giving results comparable with R-ordering based methods.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Nowadays, inspired by the great success of Transformers in Natural Language Processing, many applications of Vision Transformers (ViTs) have been investigated in the field of medical image analysis including breast ultrasound (BUS) image segmentation and classification. In this paper, we propose an efficient multi-task framework to segment and classify tumors in BUS images using hybrid convolutional neural networks (CNNs)-ViTs architecture and Multi-Perceptron (MLP)-Mixer. The proposed method uses a two-encoder architecture with EfficientNetV2 backbone and an adapted ViT encoder to extract tumor regions in BUS images. The self-attention (SA) mechanism in the Transformer encoder allows capturing a wide range of high-level and complex features while the EfficientNetV2 encoder preserves local information in image. To fusion the extracted features, a Channel Attention Fusion (CAF) module is introduced. The CAF module selectively emphasizes important features from both encoders, improving the integration of high-level and local information. The resulting feature maps are reconstructed to obtain the segmentation maps using a decoder. Then, our method classifies the segmented tumor regions into benign and malignant using a simple and efficient classifier based on MLP-Mixer, that is applied for the first time, to the best of our knowledge, for the task of lesion classification in BUS images. Experimental results illustrate the outperformance of our framework compared to recent works for the task of segmentation by producing 83.42% in terms of Dice coefficient as well as for the classification with 86% in terms of accuracy.

This paper proposes an ultrasound breast tumor CAD system based on BI-RADS features scoring and decision tree algorithm. Because of the difficulty of biopsy label collection, the proposed system adopts a few-shot learning method. The SVM classifier is employed to preliminarily mark the unlabeled cases firstly. Then these unlabeled cases with the pseudo labels are combined with the few real-labeled cases to train the decision tree. To test the performance of the proposed method, 1208 ultrasound breast images were collected, and three well-experienced clinicians and three interns evaluated these images according to the BI-RADS scoring scheme. All of the images are transformed into vectors such that the algorithm can process. The experimental results show that the system performance improves significantly with the help of pseudo-labeled data. Compared to the decision tree trained by the real-labeled cases only, when the number of real-labeled cases was 40, the accuracy, specificity, sensitivity of the proposed system were increased by 2.05%, 2.47% and 1.81%, respectively; the positive predictive value (PPV) and the negative predictive value (NVP) were increased by 1.29% and 3.05%, respectively. Meanwhile, the performance of the proposed method was the same as the method using sufficient samples. When the number of the labeled cases reached 100, the accuracy, specificity, sensitivity, PPV and NVP of the proposed method were 90.03%, 87.02%, 91.68%, 93.07%, and 85.03%, respectively. The results demonstrate that our method can efficiently distinguish the breast tumor although the labeled data is not sufficient.

Cross-modal Transfer Learning Based on an Improved CycleGAN Model for Accurate Kidney Segmentation in Ultrasound Images

Breast ultrasound (BUS) imaging is an imaging modality used for the detection and diagnosis of breast lesions and it has become a crucial modality nowadays specially for providing a complementary view when other modalities (i.e. mammography) are not conclusive. However, lesion detection in ultrasound images is still a challenging problem due to the presence of artifacts such as low contrast, speckle, inhomogeneities and shadowing. In order to deal with these problems and improve diagnosis accuracy, radiologists tend to complement ultrasound imaging with elastography data. Following the prominent relevance of elastography in clinical environments, it is reasonable to assume that lesion segmentation methods could also benefit from this complementary information. This paper proposes a novel breast ultrasound lesion segmentation framework for B-mode images including elastography information. A distortion field is estimated to restore the ideal image while simultaneously identifying regions of similar intensity inhomogeneity using a Markov Random Field (MRF) and a maximum a posteriori (MAP) formulation. Bivariate Gaussian distributions are used to model both B-mode and elastography information. This paper compares the fused B-mode and elastography framework with B-mode or elastography alone using different cases, including illustrative cases, where B-mode shows a well defined lesion and where elastography provides more meaningful information, showing a significant improvement when B-mode images are not conclusive which is often the case in non cystic lesions. Results show that combining both B-mode and elastography information in an unique framework makes the algorithm more robust and image quality independent.

Canny edge detection is widely used in image processing. Conventional Canny edge detection is commonly realized in CPUs, GPUs or FPGA fabrics alone, but CPUs and GPUs are not suitable for low-power applications. And existing edge detection implementations on specific FPGA model have little compatibility with other models. In this paper, we propose an effective Canny edge detection system consist of FPGA and ARM-SoC. ARM core is embedded with Yocto Linux OS, and FPGA is in charge of image processing and VGA display. FPGA IP cores are designed to communicate through Avalon-ST Bridge. In the end, the system has been implemented and tested with a quantity of images. The testing results show the efficiency of the proposed system design scheme and the system can be based on to achieve more complex algorithm.

EH-former: Regional easy-hard-aware transformer for breast lesion segmentation in ultrasound images

Breast cancer is a leading global cancer where early detection is critical for patient survival. While Automated Breast Ultrasound (ABUS) improves imaging over conventional methods, the manual analysis of its large 3D volumes is a time-consuming process that delays diagnosis. Deep learning can automate this segmentation task; however, existing architectures often achieve high accuracy at the expense of efficiency, resulting in large models with high parameter counts. This research addresses the dual challenge of accuracy and efficiency by proposing a novel, lightweight architecture: the ConvMixer Multi-scale Residual Attention Half U-Net (CMRA-Net). Our model enhances the efficient Half-UNet framework by integrating Ghost Module V2, ConvMixer, and Multi-scale Residual Attention (MRA) modules to enrich feature representation while minimizing computational cost. The proposed CMRA-Net was trained and evaluated on the public Breast Ultrasound Image (BUSI) dataset. It achieved a high F1Score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 5. 1 0 \%}$</tex> and an Intersection-over-Union (IoU) of 76.10%. Critically, it accomplished this with a highly efficient parameter count of only 0.21 million. These results demonstrate that CMRA-Net surpasses eight previous state-of-the-art methods, including U-Net, U-Net++, and Half-UNet, in both segmentation accuracy and memory efficiency. This work presents a significant step toward developing practical and effective automated tools for early breast cancer diagnosis.

Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively extract representative features from lesions and the background in BUS images. A novel automatic tumor segmentation method is proposed by combining a dilated fully convolutional network (DFCN) with a phase-based active contour (PBAC) model. The DFCN is an improved fully convolutional neural network with dilated convolution in deeper layers, fewer parameters, and batch normalization techniques; and has a large receptive field that can separate tumors from background. The predictions made by the DFCN are relatively rough due to blurry boundaries and variations in tumor sizes; thus, the PBAC model, which adds both region-based and phase-based energy functions, is applied to further improve segmentation results. The DFCN model is trained and tested in dataset 1 which contains 570BUS images from 89 patients. In dataset 2, a 10-fold support vector machine (SVM) classifier is employed to verify the diagnostic ability using 460 features extracted from the segmentation results of the proposed method. Advantages of the present method were compared with three state-of-the-art networks; the FCN-8s, U-net, and dilated residual network (DRN). Experimental results from 170BUS images show that the proposed method had a Dice Similarity coefficient of 88.97±10.01%, a Hausdorff distance (HD) of 35.54±29.70 pixels, and a mean absolute deviation (MAD) of 7.67±6.67 pixels, which showed the best segmentation performance. In dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.795 which is similar to the classification using the manual segmentation results. The proposed automatic method may be sufficiently accurate, robust, and efficient for medical ultrasound applications.

In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.

Automatic superpixel-based segmentation method for breast ultrasound images

&lt;p&gt;Brain tumor is a typical mass of tissue that develops when cells proliferate and divide excessively. Brain tumor perception requires a great deal of work and experience from the medical professional in order to identify the tumor's precise location. If a brain tumor is not discovered in a timely manner, it affects a person's ability to function normally and raises the death rate. This study focuses on tumor segmentation and tumor detection using magnetic resonance imaging (MRI) images. This work helps the medical professional to precisely identify the tumor location and segmentation process provides cost effective data storage. The YOLOv8s model is utilized for tumor identification, while the image segmentation technique is employed for tumor segmentation. The images come from an open-source dataset used for research, and Roboflow 100 transforms them into .yaml files that are congenial with YOLOv8s. To train the model the dataset is split into training, validation and testing. Proposed model consist of dataset which comprises 639 images, of which 453 are utilized for training, 122 for validation, and 64 for testing, resulting in a ratio of 71:19:10. The dataset is subjected to preprocessing and augmentation. The suggested model performance is assessed depending on the parameters like precision, recall, mAP50 and mAP50-95.&lt;/p&gt;