Ultrasound scan (US) imagery is an important tool for radiologists to make a fast and reliable diagnosis decision about breast lesion status (benign or malignant). Accurate and automatic segmentation of breast lesion is critical for annotating the lesion characteristics such as margin smoothness and regularity in support of the diagnosis decision. Fully convolutional network (FCN) is one of the commonly used deep learning neural network methods for semantic segmentation. This paper is concerned with effective adaptation of the FCN solutions for segmenting breast lesions from 2D ultrasound images. The paper aims to first evaluate the existing FCN solution for solving the problem at hand and compare its performance with another popular method using U-Net. The paper then highlights one key issue with the FCN, i.e. false positive pixels near the boundary of a lesion and false positive pixels forming false lesions. The paper then investigates several methods in reducing such false positive pixels, including the use of data augmentation in training the classification model and use of loss functions in training the models. Experimental results using several data sets collected from various sources show that our adapted FCN method outperforms U-Net-based solutions in general and the false positive reduction methods we attempted have reduced the false positive pixels in both regions close to lesion boundary and separate from true lesion regions.
The border irregularity of lesions or tumours is an important sign (or feature) contributing to the prediction of the tumor malignancy. This paper is concerned with developing automatic computer vision methods for assessing and recognizing thyroid nodule border irregularity from ultrasound images. Unlike many existing schemes, our methods rely on a small set of points on the nodule border marked manually by clinicians. To mitigate the absence of a fully segmented lesion boundary, we first apply the cubic-spline interpolation of the region of interest (ROI) points to approximate the lesion border and then select equal numbers of points from the approximated border using equal angular distances. We developed two complementary approaches to investigate the global (big indentations and protrusions) and local (small zigzag) irregularity features of the nodule. The first approach includes two Euclidian distances-based methods and a method inspired by Fractal Dimensions (FD). The distances-based methods facilitate the use of the interpolated border and their radial distance functions measured from ROI points to a reference point (centroid) or reference shape (Convex hull), while the FD inspired method uses interpolated border and a fitted ellipse perimeter ratio to calculate an irregularity index. The second approach facilitates the texture analysis within the constructed ribbons around the border line of different widths using feature vector of uniform local binary pattern (ULBP). We evaluate and compare the performance of our methods from the two approaches by using two datasets consisting of 395 and 100 ultrasound images of thyroid nodules collected from two hospitals and labelled by experienced radiologists respectively. The first is used as training and internal testing set, while the second is used as an external testing set. We shall show the viability of our methods attaining accuracy rates between 70% and 90%.
The difficulty of obtaining sufficient number of appropriately labelled samples is a major obstacle to learning class discriminating features by Machine Learning (ML) algorithms for tumor diagnostics from Ultrasound (US) images. This is often mitigated by sample augmentation, whereby new samples are generated from existing samples by rotation and flipping operations, Singular Value Decomposition (SVD) or generating synthetic image by Generative Adversarial Networks (GANs). The first approach does not generate new genuine samples, SVD generates images may not be easy to recognize as US tumor scans, and while GANs generate images are visually convincing their use for diagnostics may lead to overfitting and subject to adversarial attacks. We propose an innovative sample augmentation approach that utilizes our recently developed Tumor Margin Appending (TMA) scheme. The TMA scheme constructs the Convex Hull (CH) of the tumor region using a small set of radiologist marked tumor boundary points and crops the image at different radial expansion ratios of the CH onto surrounding tissue. Various ML algorithms, handcrafted features and Convolutional Neural Network (CNN), trained with TMA images at different ratios achieved acceptable diagnostic accuracies. In this paper, our sample augmentation scheme expands the ML training datasets by including TMA samples at several expansion ratios. Results of experiments on training CNN tumor diagnostic schemes for breast tumors yield improved classification performance with additional benefits, including robustness against different inadvertently practiced cropping at different hospitals, serves as a regularizer to reduce model overfitting when tested on unseen datasets obtained using unknown tumor segmentation and cropping procedure.
Inspection of glass façade and concrete structures for damages such as cracks are crucial for buildings safety and maintenance. Such surface cracks result in appearance of objects of abnormal geometric shapes, an obvious motivation to investigate geometric descriptor features associated with visible objects on surfaces of such materials. Here, we are concerned with developing generic automatic vision-based inspection for detection and recognition of such infrastructure anomalies. This paper extends and enhances our earlier work on glass façade cracks. We propose and test the performance of several handcrafted texture feature descriptors to discriminate cracked surface material. These features include the Histogram of Oriented Gradients (HOG), the Uniform Local Binary Pattern (ULBP), together with quantised Linearity and curvature measures obtained post an edge detection procedure. Unlike the previous paper, we extract and concatenate these features from a 3x3 blocks that partition the input image. The performance of the proposed methods is tested on four datasets of glass cracks and a large dataset of concrete cracks. We shall demonstrate that the block-based approach yields significant (5%-10%) improvement compared to our earlier work on glass, and all features have high performance for concrete surfaces attaining 98.6% for HOG feature. Furthermore, we adapt CNN layers trained on the ImageNet dataset and transfer this knowledge to the crack surface recognition task. The significant efficiency of handcrafted features, compared to CNN models, raises issues on models suitability for implementation on board UAV and constrained mobile devices.
In tumor diagnostics from Ultrasound scan images, the region of interest is often determined by marking the boundary of the suspect mass by experts, simply by clicking on sufficient number of tumor boundary points. To determine whether the tumor is malignant or benign, clinical experts who are trained for long time on how to interpret image information from the marked tumor region and from the surrounding area. In contrast, in designing automatic computer aided diagnosis system using both traditional and conventional machine learning, the relevant image features are generally obtained by cropping the tumor as region of interest (RoI) without considering the periphery of the tumor that might contain important discriminative information for better classification accuracy. In this work, we investigate the impact on classification accuracy of different types of tumors by the cropping strategy where the tumor area will be augmented by a proportion of the surrounding region of the ROI. The required optimal proportion need to be determined so that the cropped ROIs encapsulate information about posterior echo and shadow of the tumor in addition to internal texture and echo that has mainly been used as classification indicators. Recently proposed cropping techniques use the best fitting ellipse of the tumor and examine the proportion by which the ellipse is expanded to improve accuracy. Unfortunately, the fitting ellipse may not reflect the shape of the tumor. Here, we investigate a number of alternative approaches of cropping the ROIs using the concept of convex hull shape(s) determined from the tumor boundary points selected by radiologists. Initially, we check several expansion ratio scales of the convex hull ranging from 0.6 to 4.0 against the cropped tumor without margin. Several classification methods including handcrafted features and deep learning methods are adopted for breast and liver tumors using ultrasound images. We shall demonstrate the importance of optimal cropping for breast and liver ultrasound tumor classification. Furthermore, optimal margin depends on the cancer type and classification method as well.
The number of active landmines is uncertain, however, estimated 5,554 people were killed or injured by mines in 2019. Understanding the land-cover before mine clearance process provides valuable information on the scale of the problem, the resources to clear the field and ensures all hazardous areas prioritized. In this paper, we present a new framework for land clearness prioritization using land-cover analysis. We use remote sensing images from sentinel-2 to estimate the changes in the land cover. Specifically, we estimate the changes in vegetation and non-vegetation areas. Further, we use the amount and number of land changes during a period to provide recommendations on the clearance priority for different areas. A case study for different areas in the Kingdom of Cambodia is presented with several observations of satellite images for the years 2019 and 2020. Several suspected hazardous areas (or polygons) are defined by landmine surveying expert for analysis. A change matrix for each polygon is obtained from consecutive observations. Then, a series of qualitative and quantitative 2- dimensional characteristics are extracted such as class change mask from-to, percentage loss and gain per class. The 2D characteristics, together with expert-defined scores of class-change importance are used to compute the amount and number of changes in each polygon and a recommendation on the clearance priorities. Our study demonstrates that analysing the changes in land-cover is a promising direction to help in the non-technical survey process and increasing the productivity of the land release.
Manual inspections of glass façade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis and availability of affordable unmanned aerial vehicles (UAVs) with onboard video recording and processing sensors provide opportunities for smart, safe and automatic glass façade inspections. This paper is concerned with developing an effective solution for recognizing cracked glass panels, which can be installed on board a UAV. From static 2D photographic images, the proposed solution analyzes textural patterns of smooth glass surface and crack segments, linearity of detected crack segments, geometrical characteristics of crack curvatures and the crack pixel patterns, captures these discriminative features for glass cracks using Uniform Local Binary Pattern (ULBP), histograms of linearity, geometrical curvature descriptors with fixed length connected pixel configurations, and accordingly classifies images of cracked and non-cracked glass panels using a kNN classifier. Experimental results with images of different resolutions acquired by a UAV drone in a real office building setting and images collected through Google search demonstrate that the proposed solution achieves promising results with accuracy rates in excess of 80% and even as high as 91% despite the presence of reflections.
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