Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN

The accurate segmentation and identification of vertebrae presents the foundations for spine analysis including fractures, malfunctions and other visual insights. The large-scale vertebrae segmentation challenge (VerSe), organized as a competition at the Medical Image Computing and Computer Assisted Intervention (MICCAI), is aimed at vertebrae segmentation and labeling. In this paper, we propose a framework that addresses the tasks of vertebrae segmentation and identification by exploiting both deep learning and classical machine learning methodologies. The proposed solution comprises two phases: a binary fully automated segmentation of the whole spine, which exploits a 3D convolutional neural network, and a semi-automated procedure that allows locating vertebrae centroids using traditional machine learning algorithms. Unlike other approaches, the proposed method comes with the added advantage of no requirement for single vertebrae-level annotations to be trained. A dataset of 214 CT scans has been extracted from VerSe’20 challenge data, for training, validating and testing the proposed approach. In addition, to evaluate the robustness of the segmentation and labeling algorithms, 12 CT scans from subjects affected by severe, moderate and mild scoliosis have been collected from a local medical clinic. On the designated test set from Verse’20 data, the binary spine segmentation stage allowed to obtain a binary Dice coefficient of 89.17%, whilst the vertebrae identification one reached an average multi-class Dice coefficient of 90.09%. In order to ensure the reproducibility of the algorithms hereby developed, the code has been made publicly available.

Improved Connected Component Labeling Algorithm for Remote Sensing Image Classification

The main objective of this paper is to carry out a detailed analysis of the most popular Connected Component Labeling (CCL) algorithms for remote sensing image classification. This algorithm searches line-by-line, top to bottom to assign a splotch label to each current pixel that is connected to a splotch. This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. It assigns a label to a new object, most labeling algorithms use a scanning step that examines some of its neighbors. The first strategy deeds the dependencies among the neighbors to reduce the number of neighbors examined. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based deep rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. The connected component labeling assigns labels to a pixel such that adjacent pixels of the same features are assigned the same label. The paper presents a modification of this algorithm that allows the resolution of merged labels and experimental results demonstrate that proposed method is much more efficient than conventional methods for various kinds of color images. This method is improving the labeling algorithms and also benefits for other applications in computer vision and pattern recognition

Smart hand gestures recognition using K-NN based algorithm for video annotation purposes

<span>Sign languages are the most basic and natural form of languages which were used even before the evolution of spoken languages. These sign languages were developed using various sign "gestures" that are made using hand palm. Such gestures are called "hand gestures". Hand gestures are being widely used as an international assistive communication method for deaf people and many life aspects such as sports, traffic control and religious acts. However, the meanings of hand gestures vary among different civilization cultures. Therefore, because of the importance of understanding the meanings of hand gestures, this study presents a procedure whichcan translate such gestures into an annotated explanation. The proposed system implements image and video processing which are recently conceived as one of the most important technologies. The system initially, analyzes a classroom video as an input, and then extracts the vocabulary of twenty gestures. Various methods have been applied sequentially, namely: motion detection, RGB to HSV conversion, and noise removing using labeling algorithms. The extraction of hand parameters is determined by a K-NN algorithm to eventually determine the hand gesture and, hence showing their meanings. To estimate the performance of the proposed method, an experiment using a hand gesture database is performed. The results showed that the suggested method has an average recognition rate of 97%. </span>

Temporal structure of mouse courtship vocalizations facilitates syllable labeling

Mice emit sequences of ultrasonic vocalizations (USVs) but little is known about the rules governing their temporal order and no consensus exists on the classification of USVs into syllables. To address these questions, we recorded USVs during male-female courtship and found a significant temporal structure. We labeled USVs using three popular algorithms and found that there was no one-to-one relationships between their labels. As label assignment affects the high order temporal structure, we developed the Syntax Information Score (based on information theory) to rank labeling algorithms based on how well they predict the next syllable in a sequence. Finally, we derived a novel algorithm (Syntax Information Maximization) that utilizes sequence statistics to improve the clustering of individual USVs with respect to the underlying sequence structure. Improvement in USV classification is crucial for understanding neural control of vocalization. We demonstrate that USV syntax holds valuable information towards achieving this goal.

Automatic labeling algorithms and their applications in weld seam of oil pipeline

Building information model (BIM) technology has become an important tool for construction practitioners to improve the engineering design, construction and management. However, it has not been widely used in petrochemical industry, such as oil pipeline construction. This paper presents an automatic labeling algorithm based on BIM technology. We used simulated annealing algorithm to optimize the overlap of pipe weld label positions, resulting in the sequential pipe welds. We used this algorithm in oil pipeline construction and verified its automatic labeling effect on pipe welds.

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

A novel data-driven approach is proposed for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO3, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern-by-pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms.

Generating a Cylindrical Panorama from a Forward-Looking Borehole Video for Borehole Condition Analysis

Geological exploration plays a fundamental and crucial role in geological engineering. The most frequently used method is to obtain borehole videos using an axial view borehole camera system (AVBCS) in a pre-drilled borehole. This approach to surveying the internal structure of a borehole is based on the video playback and video screenshot analysis. One of the drawbacks of AVBCS is that it provides only a qualitative description of borehole information with a forward-looking borehole video, but quantitative analysis of the borehole data, such as the width and dip angle of fracture, are unavailable. In this paper, we proposed a new approach to create a whole borehole-wall cylindrical panorama from the borehole video acquired by AVBCS, which provides a possibility for further analysis of borehole information. Firstly, based on the Otsu and region labeling algorithms, a borehole center location algorithm is proposed to extract the borehole center of each video image automatically. Afterwards, based on coordinate mapping (CM), a virtual coordinate graph (VCG) is designed in the unwrapping process of the front view borehole-wall image sequence, generating the corresponding unfolded image sequence and reducing the computational cost. Subsequently, based on the sum of absolute difference (SAD), a projection transformation SAD (PTSAD), which considers the gray level similarity of candidate images, is proposed to achieve the matching of the unfolded image sequence. Finally, an image filtering module is introduced to filter the invalid frames and the remaining frames are stitched into a complete cylindrical panorama. Experiments on two real-world borehole videos demonstrate that the proposed method can generate panoramic borehole-wall unfolded images from videos with satisfying visual effect for follow up geological condition analysis. From the resulting image, borehole information, including the rock mechanical properties, distribution and width of fracture, fault distribution and seam thickness, can be further obtained and analyzed.