Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections

Maggioni, Mauro; Davis, Gustave L.; Warner, Frederick; Geshwind, Frank; Coppi, Andreas; Deverse, R. A.; Coifman, Ronald R.

doi:10.1117/12.646078

Cited by 24 publications

(21 citation statements)

References 5 publications

(6 reference statements)

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“…where r c ; r r , and r s are correlation coefficients for C, R, and S. CI depicted CLBP ( Maggioni et al [57] presented a classification technique for discriminating normal, precancerous, and cancerous states of colon. In their work, tissue specimens are stained using H&E technique.…”

Section: Hyperspectral Analysis-based Techniquesmentioning

confidence: 99%

“…Contrary, laser-induced fluorescence-based techniques [74], [75], [76], [77], [78], [79], [80], [81], [82], [83] have smaller accuracy, because equipment is quite delicate and a minute human error leads to wrong results. Texture [35], [36], [37], [38], [39], [40], [41], spectral [53], [54], [55], [56], [57], [58], [59], and gene analysis-based techniques [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73] show moderate accuracy. Second factor is availability of equipment.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Focus of a few studies is to perform hyperspectral analysis on colon biopsy images, and classify samples into different classes [53], [54], [55], [56], [57], [58], [59]. Another interesting and quite promising method of colon cancer detection is simultaneous analysis of thousands of human genes to detect genetic alterations responsible for cancer [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73].…”

Section: Introductionmentioning

confidence: 99%

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A Recent Survey on Colon Cancer Detection Techniques

Rathore

Hussain

Ali

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.

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Section: Hyperspectral Analysis-based Techniquesmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Recent Survey on Colon Cancer Detection Techniques

Rathore

Hussain

Ali

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…In numerical experiments, a 256 × 256 × 15 spectral image was accurately recovered from a 256 × 256 observation using an appropriate reconstruction approach. In related work, objects are illuminated by light sources with tunable spectra using spatial light modulators to facilitate compressive spectral image acquisition [10]. Task-specific information compressive imagers: Compressive optical architectures have been considered for target detection systems, which use knowledge of the target's features to reduce the number of measurements required from a more conventional snapshot camera.…”

Section: Architectures For Compressive Image Acquisitionmentioning

confidence: 99%

Compressive Optical Imaging: Architectures and Algorithms

Marcia

Willett

Harmany

2011

Optical and Digital Image Processing

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Many traditional optical sensors are designed to collect directly interpretable and intuitive measurements. For instance, a standard digital camera directly measures the intensity of a scene at different spatial locations to form a pixel array. Recent advances in the fields of image reconstruction, inverse problems, and compressive sensing (CS) [1,2] indicate, however, that substantial performance gains may be possible in many contexts via less direct measurements combined with computational methods. In particular, CS allows for the extraction of high-resolution images from relatively small focal plane arrays (FPAs). CS is described in detail in Chapter 23. The basic idea of CS theory is that when the image of interest is very sparse or highly compressible in some basis (i.e., most basis coefficients are small or zero valued), relatively few well-chosen observations suffice to reconstruct the most significant nonzero components. In particular, judicious selection of the type of image transformation introduced by measurement systems may significantly improve our ability to extract high-quality images from a limited number of measurements. By designing optical sensors to collect measurements of a scene according to CS theory, we can use sophisticated computational methods to infer critical scene structure and content.These ideas are particularly relevant to imaging applications in which it is useful or even crucial to keep the FPA of an optical system relatively small. For example, in low-light settings where sensitive detectors are costly, smaller FPAs translate directly to less expensive systems. Smaller FPAs also make systems lighter and, thus, more portable. In addition, imaging systems with fewer photodetectors consume less power and, therefore, require fewer battery charges. Finally, smaller cameras can fit into tighter spaces for unobtrusive surveillance. An important goal in the design of many imaging systems, then, is to extract as much information as possible from a small number of detector array measurements.While recent progress in the exploitation of CS theory is highly encouraging, there are several key issues in the context of optical systems that must be addressed:Optical and Digital Image Processing: Fundamentals and Applications, First Edition. Edited

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“…In related work, objects are illuminated by light sources with tunable spectra using spatial light modulators to facilitate compressive spectral image acquisition. 46 Finally, we mention more recent examples of compressive spectral imagers, such as compressive structured light codes where each camera pixel measures light from points along the line of sight within a volume density, 47 and cameras that use dispersers for imaging piecewise "macropixel" objects (e.g., biochip microarrays in biochemistry). …”

Section: Spectral Imagersmentioning

confidence: 99%

Compressed sensing for practical optical imaging systems: a tutorial

2011

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Abstract. The emerging field of compressed sensing has potentially powerful implications for the design of optical imaging devices. In particular, compressed sensing theory suggests that one can recover a scene at a higher resolution than is dictated by the pitch of the focal plane array. This rather remarkable result comes with some important caveats however, especially when practical issues associated with physical implementation are taken into account. This tutorial discusses compressed sensing in the context of optical imaging devices, emphasizing the practical hurdles related to building such devices, and offering suggestions for overcoming these hurdles. Examples and analysis specifically related to infrared imaging highlight the challenges associated with large format focal plane arrays and how these challenges can be mitigated using compressed sensing ideas. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3596602] Subject terms: compressed sensing; sampling; image reconstruction; inverse problems; computational imaging; infrared; coded aperture imaging; optimization.Paper 100978TR received Nov. 29, 2010; revised manuscript received May 8, 2011; accepted for publication May 12, 2011; published online Jul. 6, 2011. IntroductionThis tutorial describes new methods and computational imagers for increasing system resolution based on recently developed compressed sensing (CS, also referred to as compressive sampling) 1, 2 techniques. CS is a mathematical framework with several powerful theorems that provide insight into how a high resolution image can be inferred from a relatively small number of measurements using sophisticated computational methods. For example, in theory a 1 mega-pixel array could potentially be used to reconstruct a 4 mega-pixel image by projecting the desired high resolution image onto a set of low resolution measurements (via spatial light modulators, for instance) and then recovering the 4 mega-pixel scene through sparse signal reconstruction software. However, it is not immediately clear how to build a practical system that incorporates these theoretical concepts. This paper provides a tutorial on CS for optical engineers which focuses on 1. a brief overview of the main theoretical tenets of CS, 2. physical systems designed with CS theory in mind and the various tradeoffs associated with these systems, and 3. an overview of the state-of-the-art in sparse reconstruction algorithms used for CS image formation. There are several other tutorials on CS available in the literature which we highly recommend; 3-7 however, these papers do not address important technical issues related to optical systems, including a discussion of the tradeoffs associated with non-negativity, photon noise, and the practicality of implementation in real imaging systems.Although the CS theory and methods we describe in this paper can be applied to many general imaging systems, we concentrate on infrared (IR) technology as a specific example to highlight the challenges associated...

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Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections

Cited by 24 publications

References 5 publications

A Recent Survey on Colon Cancer Detection Techniques

A Recent Survey on Colon Cancer Detection Techniques

Compressive Optical Imaging: Architectures and Algorithms

Compressed sensing for practical optical imaging systems: a tutorial

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