Evaluation of bone texture imaging parameters on panoramic radiographs of patients with Sheehan’s syndrome: a STROBE-compliant case-control study

Summary Sheehan’s syndrome (SHS) is a rare condition related to the risk of osteoporosis and evaluation of bone texture imaging features on panoramic radiographs would be suitable for this condition, which was the aim of the present study. Fractal dimension, lacunarity, and trabecular morphologic aspects were significantly altered in these patients. Introduction SHS is an important public health problem particularly in developing countries. It is characterized as postpartum hypopituitarism secondary to obstetric complications-related ischemic pituitary necrosis that shows significant systemic metabolic repercussions. Thus, this study aimed to evaluate bone texture parameters in digital panoramic radiographs of patients with SHS. Methods A case-control study was conducted with 30 SHS patients from an Endocrinology and Diabetology Service of reference in Brazil, and 30 age- and sex-matched healthy controls. A custom computer program measured fractal dimension, lacunarity, and some morphologic features in the following mandibular regions of interest (50 × 50 pixels): below the mental foramen (F1), between the first and second molars (M1), and at the center of the mandibular ramus (R1). Results The fractal analysis showed a statistically significant difference between the studied groups in all regions of interest. The fractal dimension in F1 (p = 0.016), M1 (p = 0.043), and R1 (p = 0.028) was significantly lower in SHS group, as well as lacunarity in R1 (p = 0.008). Additionally, several morphologic features were statistically significant in the SHS group (p < 0.05). Conclusion Therefore, individuals with SHS showed altered imaging texture parameters on panoramic radiographs, which reflect a smaller spatial organization of the bone trabeculae and, possibly, a state of reduced mineral bone density

Fractal block coding techniques in image compression

Fractal block coding is a relatively new scheme for image compression. In this dissertation, several ádvanced schemes are proposed based upon Jacquin’s fractal block coding scheme. Exploiting self-similarity at different target block size levels is proposed which allows the self-similarity in the image to be exploited further. Smoother areas are coded with bigger target block sizes while fíne details are coded with smaller target block sizes. More image parts coded at a higher coding level will result in a lower bit rate. Removal of affine-block-wise self-similarity is proposed which includes block-wise self-similarity as a special case. With the utilisation of affineblock-wise self-similarity, the library is substantially enriched which results in a higher probability of coding a target block at a higher coding level. A very fast multi-level fractal block coding scheme exploiting affine-block-wise selfsimilarities is proposed. In the fast coding scheme, self-similarity in the very local area of the target block to be coded is exploited. By using affine-block-wise self-similarity, local correlations are exploited to a much further extent. The number of library blocks used for coding a target block is substantially reduced which results in very fast coding scheme. The proposed fast coding scheme outperforms previous implementations of the fractal block coding technique. A hybrid fractal block coding and DCT scheme is proposed which codes a subsampled image using fractal block coding techniques. The fractal codes are used to decode by zooming to the original image size. The DCT technique is introduced to code the residue image. The proposed scheme is better than the pure fractal block coding scheme. The advanced fractal block coding schemes and the hybrid coder for still images are also applied to video compression which also give some promising simulation results

Fractal methods in image analysis and coding

In this thesis we present an overview of image processing techniques which use fractal methods in some way. We show how these fields relate to each other, and examine various aspects of fractal methods in each area. The three principal fields of image processing and analysis th a t we examine are texture classification, image segmentation and image coding. In the area of texture classification, we examine fractal dimension estimators, comparing these methods to other methods in use, and to each other. We attempt to explain why differences arise between various estimators of the same quantity. We also examine texture generation methods which use fractal dimension to generate textures of varying complexity. We examine how fractal dimension can contribute to image segmentation methods. We also present an in-depth analysis of a novel segmentation scheme based on fractal coding. Finally, we present an overview of fractal and wavelet image coding, and the links between the two. We examine a possible scheme involving both fractal and wavelet methods

A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease