Comparative analysis of spatial and transform domain methods for meningioma subtype classification

Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%

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

A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours

With the heterogeneous nature of tissue texture, using a single resolution approach for optimum classification might not suffice. In contrast, a multiresolution wavelet packet analysis can decompose the input signal into a set of frequency subbands giving the opportunity to characterise the texture at the appropriate frequency channel. An adaptive best bases algorithm for optimal bases selection for meningioma histopathological images is proposed, via applying the fractal dimension (FD) as the bases selection criterion in a tree-structured manner. Thereby, the most significant subband that better identifies texture discontinuities will only be chosen for further decomposition, and its fractal signature would represent the extracted feature vector for classification. The best basis selection using the FD outperformed the energy based selection approaches, achieving an overall classification accuracy of 91.25% as compared to 83.44% and 73.75% for the co-occurrence matrix and energy texture signatures; respectively

Meningioma classification using an adaptive discriminant wavelet packet transform

Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerised histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each subband provides different information pertaining to the texture in the image sample. Our novel method, the Adaptive Discriminant Wavelet Packet Transform (ADWPT), provides a means for selecting the most useful subbands and hence, achieves feature selection. It also provides a mechanism for ranking features based upon the discrimination power of a subband. The more discriminant a subband, the better it is for classification. The results show that high classification accuracies are obtained by selecting subbands with high discrimination power. Moreover, subbands that are more stable i.e. have a higher probability of being selected provide better classification accuracies. Stability and discrimination power have been shown to have a direct relationship with classification accuracy. Hence, ADWPT acquires a subset of subbands that provide a highly discriminant and robust set of features for Meningioma subtype classification. Classification accuracies obtained are greater than 90% for most Meningioma subtypes. Consequently, ADWPT is a robust and adaptive technique which enables it to overcome the issue of high intra-class variation by statistically selecting the most useful subbands for meningioma subtype classification. It overcomes the issue of low inter-class variation by adapting to texture samples and extracting the subbands that are best for differentiating between the various meningioma subtype textures

Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier

Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions

Texture analysis (TA) of histological images has recently received attention as an automated method of characterizing liver fibrosis. The colored staining methods used to identify different tissue components reveal various patterns that contribute in different ways to the digital texture of the image. A histological digital image can be represented with various color spaces. The approximation processes of pixel values that are carried out while converting between different color spaces can affect image texture and subsequently could influence the performance of TA. Conventional TA is carried out on grey scale images, which are a luminance approximation to the original RGB (Red, Green, and Blue) space. Currently, grey scale is considered sufficient for characterization of fibrosis but this may not be the case for sophisticated assessment of fibrosis or when resolution conditions vary. This paper investigates the accuracy of TA results on three color spaces, conventional grey scale, RGB, and Hue-Saturation-Intensity (HSI), at different resolutions. The results demonstrate that RGB is the most accurate in texture classification of liver images, producing better results, most notably at low resolution. Furthermore, the green channel, which is dominated by collagen fiber deposition, appears to provide most of the features for characterizing fibrosis images. The HSI space demonstrated a high percentage error for the majority of texture methods at all resolutions, suggesting that this space is insufficient for fibrosis characterization. The grey scale space produced good results at high resolution; however, errors increased as resolution decreased