In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

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Survey over image thresholding techniques and quantitative performance evaluation

A review on image segmentation techniques

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.

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Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Color image segmentation: advances and prospects

Five properties of texture, namely, coarseness, contrast, business, complexity, and texture strength, are given conceptual definitions in terms of spatial changes in intensity. These conceptual definitions are then approximated in computational forms. In comparison with human perceptual measurements, the computational measures have shown good correspondences in the rank ordering of ten natural textures. The extent to which the measures approximate visual perception was investigated in the form of texture similarity measurements. These results were also encouraging, although not as good as in the rank ordering of the textures. The differences may be due to the complex mechanism of human usage of multiple cues. Improved classification results were obtained using the above features as compared with two existing texture analysis techniques. The application of the features in agricultural land-use classification is considered. >

Textural features corresponding to textural properties

The effectiveness of the theory of fuzzy sets in detecting different regional boundaries of X-ray images is demonstrated. The algorithm includes a prior enhancement of the contrast among the regions (having small change in gray levels) using the contrast intensification (INT) operation along with smoothing in the fuzzy property plane before detecting its edges. The property plane is extracted from the spatial domain using S, ? and (1 ?) functions and the fuzzifiers. Final edge detection is achieved using max or min operator. The system performance for different parameter conditions is illustrated by application to an image of a radiograph of the wrist.

On Edge Detection of X-Ray Images Using Fuzzy Sets

Automatic grey level thresholding through index of fuzziness and entropy

A method of image enhancement by computer using the fuzzy set theoretic approach is reported. The algorithm involves extraction of fuzzy properties corresponding to pixels and then successive application of fuzzy operator `contrast intensification´ on the property plane. System performance with different indexes of fuzziness is demonstrated for an English script input.

Image enhancement using fuzzy set

Context The ability to perform clinical procedures safely is a key skill for health care professionals. Performing such procedures on conscious patients is challenging and requires a combination of technical and communication skills. We have developed quasi-clinical scenarios, where inanimate models attached to simulated patients provide a convincing learning environment. Procedures are rated by expert observers and by the ‘patient’ and recorded for subsequent review. This study explores the potential of locating such scenarios within a real clinical setting, allowing participants to experience the challenges of the workplace while ensuring patient safety. An innovative portable digital recording device (the ‘Virtual Chaperone’) is evaluated for use in clinical settings.

Methods  A qualitative design (observation and interview studies) investigated volunteer medical students undertaking 2 procedure scenarios (insertion of urinary catheter and wound closure with sutures) within the accident unit of a large London hospital. All procedures were observed in real time and recorded digitally (using the Virtual Chaperone). A protocol was used for structured feedback. Observational and interview data was analysed using standard qualitative techniques.

Results  Seven sessions with 22 undergraduate medical students took place over 9 months within 1 centre. Data confirmed the feasibility of using a moveable, self-contained training scenario within an authentic clinical setting. Overall, the response from participants was positive.

Conclusion  Scenario-based teaching within an authentic clinical environment is feasible and perceived by participants to be educationally useful. This approach blurs traditional boundaries between skills laboratory teaching and clinical practice and may offer considerable advantages in training for clinical procedures.

R.A. King

Papers

Textural features corresponding to textural properties

On Edge Detection of X-Ray Images Using Fuzzy Sets

Automatic grey level thresholding through index of fuzziness and entropy

Image enhancement using fuzzy set

Blurring the boundaries: scenario-based simulation in a clinical setting