Publications:

• N. Alsubaie, S. E. A. Raza, and N. M. Rajpoot, “Stain Deconvoloution of Histology Images via Independent Component Analysis in the Wavelet Domain,” (to appear) in International Symposium on Biomaedical Imaging, Apr. 2016. [Abstract] [doi] [Software]

  With the ubiquity of digital slide scanners, histology image analysis is rapidly emerging as an active area of research. Several histology image analysis algorithms such as those for mitotic cell detection, nuclei segmentation and hormone re- ceptors scoring depend on colour information obtained from images of the scanned slides. However, different standards followed by different labs and the technical variation among different scanners result in stain inconsistency in histology images. Thus, applications that use colour information may fail when they are applied to images with different appearance of stain colours. In this paper, we propose a novel method to estimate the so called stain matrix via independent compo- nent analysis in the wavelet domain for stain deconvolution in histology images. Experimental results demonstrate stable and more accurate stain deconvolution results as compared to other recently proposed algorithms.

• M. N. Kashif, S. E. A. Raza, K. Sirinukunwattana, M. Arif, and N. M. Rajpoot, “Handcrafted Features with Convolutional Neural Networks for Detection of Tumor Cells in Histology Images,” (to appear) in International Symposium on Biomaedical Imaging, Apr. 2016. [Abstract] [doi] [Software]

  Detection of tumor nuclei in cancer histology images re- quires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neu- ral networks (CNNs) to detect cells in H&E stained images. However, all such methods use some form of raw pixel inten- sities as input and rely on the CNN to learn the deep features. In this work, we extend a recently proposed spatially con- strained CNN (SC-CNN) by proposing features that capture texture characteristics and show that although CNN produces good results on automatically learned features, it can perform better if the input consists of a combination of handcrafted features and the raw data. The handcrafted features are com- puted through the scattering transform which gives non-linear invariant texture features. The combination of handcrafted features with raw data produces sharp proximity maps and better detection results than the results of raw intensities with a similar kind of CNN architecture.

• S. E. A. Raza, D. Langenkämper, K. Sirinukunwattana, D. B. A. Epstein, T. W. Nattkemper, and N. M. Rajpoot, “Robust Normalization Protocols for Multiplexed Fluorescence Bioimage Analysis,” (to appear in) BMC Biodata Min., Mar. 2016. [Abstract] [doi] [Software]

  The study of mapping and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to use the standard immuno-fluorescence microscopy in a cyclic manner. Unfortunately, these techniques suffer from variability in intensity and positioning of signals from protein markers within a run and across different runs. Therefore, it is necessary to standardize protocols for preprocessing of the multiplexed bioimaging (MBI) data from multiple runs to a comparable scale before any further analysis can be performed on the data. In this paper, we compare various normalization protocols and propose on the basis of the obtained results, a robust normalization technique that produces consistent results on the MBI data collected from different runs using the Toponome Imaging System (TIS). Normalization results produced by the proposed method on a sample TIS data set for colorectal cancer patients were ranked favorably by two pathologists and two biologists. We show that the proposed method produces higher between class Kullback-Leibler (KL) divergence and lower within class KL divergence on a distribution of cell phenotypes from colorectal cancer and histologically normal samples.

• K. Sirinukunwattana, S.E.A. Raza, Y.-W. Tsang, D. Snead, I. Cree, and N.M. Rajpoot, “Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images,” IEEE Trans. Med. Imaging, pp. 1–1, Jan. 2016. [Abstract] [doi] [Software]

  Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the center of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and could potentially lead to a better understanding of cancer.

• K. Sirinukunwattana, S.E.A. Raza, Y.-W. Tsang, D. Snead, I. Cree, and N.M. Rajpoot, “Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images,” IEEE Trans. Med. Imaging, pp. 1–1, Jan. 2016. [Abstract] [doi] [Software]

  Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the center of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and could potentially lead to a better understanding of cancer.

• S.E.A. Raza, V. Sanchez, G. Prince, J. Clarkson, and N. M. Rajpoot, “Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain,” Pattern Recognit., vol. 48, pp. 2119–2128, Jul. 2015. [Abstract] [doi] [Software]

Teaching:

• 2011/2012: CS403 Multimedia Processing, Communications and Storage [Module Webpage]
• 2012/2013: CS131 Mathematics for Computer Scientists II [Module Webpage]

s.e.a.raza@warwick.ac.uk

BioImage Analysis Lab

Department of Computer Science

University of Warwick

United Kingdom

Telephone ext: 73789