Detection of human finger joints in ultrasound images: structure and optimization

Synovitis is the inflammation of a synovial membrane surrounding a joint. Its assessment is an important step in the diagnosis and treatment of rheumatoid arthritis. Joint detection is the first stage of an automated method of assessment of a degree of synovitis, from an Ultrasound (USG) image of a finger joint and its surrounding area. A joint detector consists of three parts: image preprocessing, feature extraction, and classification. Each part contains adjustable parameters that must be set experimentally to ensure the proper operation of the detector. Both the structure of a joint detector and a procedure for finding a near-optimal configuration of the adjustable parameters are described. The optimization process is based on two evaluation measures: Area Under the Receiver Operating Characteristic Curve (AUC) and False Positive Count (FPC). The optimization process decreases the number of pictures with multiple detections, which was the main point of works presented in this paper. This was achieved by increasing the number of components of the homogeneous mixed-SURF descriptor which has the greatest influence on the final result. Non-SURF descriptors achieve poorer classification results. Our research led to the creation of a better joint detector which could positively influence the final results of inflammation level classification

The molecular pattern of histopathological progression to anaplastic meningioma – A case report

Meningiomas (MGs) are the most frequent primary tumours of the central nervous system (CNS) and exhibit a large spectrum of histological types and clinical phenotypes. The WHO classification of CNS tumours established strict diagnostic criteria of the benign (Grade 1), atypical (Grade 2) and anaplastic (Grade 3) subtypes. Combined with the resection rate, WHO grading has the most crucial role as the prognostic factor. Additionally, such biomarkers as Ki-67/MIB-1, progesterone receptors and phosphor-histone H3 were correlated with MG progression. Recently, it was suggested that the aggressive behaviour of some MGs is attributed to molecular alterations, regardless of their histopathology. The analysis of loss of heterozygosity (LOH) at chromosomes 1, 9, 10, 14 and 22 was performed. The presented case of WHO Grade 2 MG initially exhibited LOH at chromosomes 10, 14 and 22. In the first recurrence, the tumour genetic profiling revealed additional LOH at chromosome 1p and atypical histopathology. During the second recurrence, an aggressive phenotype was observed and tumour progressed to an anaplastic form. Considering the appearance of the tumour relapses, the set of molecular changes overtook the histopathological progression. The genetic and histopathological imbalance in the tumour progression in secondary anaplastic MGs has not been previously described. The evolution of genetic and histopathological changes was presented in the same patient. In the future, the individualised therapy of potentially more aggressive forms of MGs could be based on certain chromosome aberrations

Detection of human finger joints in ultrasound images: structure and optimization

Synovitis is the inflammation of a synovial membrane surrounding a joint. Its assessment is an important step in the diagnosis and treatment of rheumatoid arthritis. Joint detection is the first stage of an automated method of assessment of a degree of synovitis, from an Ultrasound (USG) image of a finger joint and its surrounding area. A joint detector consists of three parts: image preprocessing, feature extraction, and classification. Each part contains adjustable parameters that must be set experimentally to ensure the proper operation of the detector. Both the structure of a joint detector and a procedure for finding a near-optimal configuration of the adjustable parameters are described. The optimization process is based on two evaluation measures: Area Under the Receiver Operating Characteristic Curve (AUC) and False Positive Count (FPC). The optimization process decreases the number of pictures with multiple detections, which was the main point of works presented in this paper. This was achieved by increasing the number of components of the homogeneous mixed-SURF descriptor which has the greatest influence on the final result. Non-SURF descriptors achieve poorer classification results. Our research led to the creation of a better joint detector which could positively influence the final results of inflammation level classification

The correlation of clinical and chromosomal alterations of benign meningiomas and their recurrences

Meningiomas (MGs) are the frequent benign intracranial tumors. Their complete removal does not always guarantee relapse-free survival. Recurrence-associated chromosomal anomalies in MGs haves been proposed as prognostic factors in addition to the World Health Organisation (WHO) grading, tumor size and resection rate. The aim of this study was to evaluate the frequency of deletions on chromosomes in sporadic MGs and to correlate them with the clinical findings and tumor behaviour. Along with survival, the tumor recurrence was the main endpoint. Chromosomal loss of heterozygosity (LOH) was studied. 46 benign MGs were subjected to the analysis, complete tumor resection was intended and no early mortalities were observed. Incomplete removal was related to parasagittal location and psammomatous hisptopathology (p<0.01). Chromosomal alterations were present in 82.6% of cases; LOH at 22q (67.4%) and 1p (34.8%) were the most frequent and associated with male sex (p=0.04). Molecular findings were not specific for any of the histopathologic grade. Tumor recurrence (14 of 46) correlated with tumor size (≥35mm), LOH at 1p, 14q, coexistence of LOH at 1p/14q, 10q/14q, ‘complex karyotype’ status (≥2 LOHs excluding 22q), patient age (younger <35), and Simpson grading of resection rate (≥3 of worse prognosis). The last 3 variables were independent significant prognostic factors in multivariate analysis and of the same importance in recurrence prediction (Receiver Operating Characteristic curves comparison p>0.05). Among the cases of recurrence, tumor progression was observed in 3 of 14. In 2 cases, LOH on 1p and/or coexistence of LOH 1p/14q correlated with anaplastic transformation

Detection of linear features including bone and skin areas in ultrasound images of joints.

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results

Detection of linear features including bone and skin areas in ultrasound images of joints.

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results.publishedVersio

Detection of linear features including bone and skin areas in ultrasound images of joints.

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results