Sahirzeeshan Ali | Case Western Reserve University (original) (raw)

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Papers by Sahirzeeshan Ali

International Journal of Radiation Oncology*Biology*Physics, 2009

The Journal of Urology, 2015

IEEE transactions on medical imaging, Jan 14, 2015

Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appe... more Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images. This procedure typically involves extraction of hundreds of features, which may be used to predict disease presence, aggressiveness, or outcome, from digitized images of tissue slides. Due to the "curse of dimensionality", constructing a robust and interpretable classifier is very challenging when the dimensionality of the feature space is high. Dimensionality reduction (DR) is one approach for reducing the dimensionality of the feature space to facilitate classifier construction. When DR is performed, however, it can be challenging to quantify the contribution of each of the original features to the final classification or prediction result. In QH it is often important not only to create an accurate classifier of disease presence and aggressiveness, but also to identify the features that contribute most substantially to class separability. T...

PloS one, 2014

Quantitative histomorphometry (QH) refers to the application of advanced computational image anal... more Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive...

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...

Frontiers of Medical Imaging, 2014

2013 IEEE 10th International Symposium on Biomedical Imaging, 2013

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013

Quantitative measurements of spatial arrangement of nuclei in histopathology images for different... more Quantitative measurements of spatial arrangement of nuclei in histopathology images for different cancers has been shown to have prognostic value. Traditionally, graph algorithms (with cell/nuclei as node) have been used to characterize the spatial arrangement of these cells. However, these graphs inherently extract only global features of cell or nuclear architecture and, therefore, important information at the local level may be left unexploited. Additionally, since the graph construction does not draw a distinction between nuclei in the stroma or epithelium, the graph edges often traverse the stromal and epithelial regions. In this paper, we present a new spatially aware cell cluster (SpACC1) graph that can efficiently and accurately model local nuclear interactions, separately within the stromal and epithelial regions alone. SpACC1 is built locally on nodes that are defined on groups/clusters of nuclei rather than individual nuclei. Local nodes are connected with edges which hav...

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, Jan 12, 2014

Shape based active contours have emerged as a natural solution to overlap resolution. However, mo... more Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particu...

International Journal of Radiation Oncology*Biology*Physics, 2009

The Journal of Urology, 2015

IEEE transactions on medical imaging, Jan 14, 2015

Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appe... more Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images. This procedure typically involves extraction of hundreds of features, which may be used to predict disease presence, aggressiveness, or outcome, from digitized images of tissue slides. Due to the "curse of dimensionality", constructing a robust and interpretable classifier is very challenging when the dimensionality of the feature space is high. Dimensionality reduction (DR) is one approach for reducing the dimensionality of the feature space to facilitate classifier construction. When DR is performed, however, it can be challenging to quantify the contribution of each of the original features to the final classification or prediction result. In QH it is often important not only to create an accurate classifier of disease presence and aggressiveness, but also to identify the features that contribute most substantially to class separability. T...

PloS one, 2014

Quantitative histomorphometry (QH) refers to the application of advanced computational image anal... more Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive...

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...

Frontiers of Medical Imaging, 2014

2013 IEEE 10th International Symposium on Biomedical Imaging, 2013

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digi... more Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of...

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013

Quantitative measurements of spatial arrangement of nuclei in histopathology images for different... more Quantitative measurements of spatial arrangement of nuclei in histopathology images for different cancers has been shown to have prognostic value. Traditionally, graph algorithms (with cell/nuclei as node) have been used to characterize the spatial arrangement of these cells. However, these graphs inherently extract only global features of cell or nuclear architecture and, therefore, important information at the local level may be left unexploited. Additionally, since the graph construction does not draw a distinction between nuclei in the stroma or epithelium, the graph edges often traverse the stromal and epithelial regions. In this paper, we present a new spatially aware cell cluster (SpACC1) graph that can efficiently and accurately model local nuclear interactions, separately within the stromal and epithelial regions alone. SpACC1 is built locally on nodes that are defined on groups/clusters of nuclei rather than individual nuclei. Local nodes are connected with edges which hav...

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, Jan 12, 2014

Shape based active contours have emerged as a natural solution to overlap resolution. However, mo... more Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particu...