M. Stella Atkins

A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies

The popularity of computer games has exploded in recent years, yet methods of evaluating user emotional state during play experiences lag far behind. There are few methods of assessing emotional state, and even fewer methods of quantifying emotion during play. This paper presents a novel method for continuously modeling emotion using physiological data. A fuzzy logic model transformed four physiological signals into arousal and valence. A second fuzzy logic model transformed arousal and valence into five emotional states relevant to computer game play: boredom, challenge, excitement, frustration, and fun. Modeled emotions compared favorably with a manual approach, and the means were also evaluated with subjective self-reports, exhibiting the same trends as reported emotions for fun, boredom, and excitement. This approach provides a method for quantifying emotional states continuously during a play experience.

A continuous and objective evaluation of emotional experience with interactive play environments

Researchers are using emerging technologies to develop novel play environments, while established computer and console game markets continue to grow rapidly. Even so, evaluating the success of interactive play environments is still an open research challenge. Both subjective and objective techniques fall short due to limited evaluative bandwidth; there remains no corollary in play environments to task performance with productivity systems. This paper presents a method of modeling user emotional state, based on a users physiology, for users interacting with play technologies. Modeled emotions are powerful because they capture usability and playability through metrics relevant to ludic experience; account for user emotion; are quantitative and objective; and are represented continuously over a session. Furthermore, our modeled emotions show the same trends as reported emotions for fun, boredom, and excitement; however, the modeled emotions revealed differences between three play conditions, while the differences between the subjective reports failed to reach significance.

Irregularity index: A new border irregularity measure for cutaneous melanocytic lesions

One of the important clinical features that differentiates benign melanocytic nevi from malignant melanomas is the irregularity of the lesion border. There are two types of border irregularities: texture irregularities, the small variations along the border, and structure irregularities, the global indentations and protrusions. Texture irregularities are subject to noise, whereas structure irregularities may suggest excessive cell growth or regression of a melanoma. We have designed a new algorithm for measuring the structure irregularities in the border. Our algorithm first locates all the local and global indentations and protrusions and organizes them in a hierarchical structure. Then an area-based index, called the irregularity index, is computed for each indentation and protrusion along the border. From the individual irregularity indices, two important new measures, the most significant irregularity index and the overall irregularity index are derived. These two new indices provide a measure of the degree of irregularity along the lesion border. A double-blinded test was performed to examine the effectiveness of these two new indices. Fourteen experienced dermatologists were asked to evaluate the borders of 40 pigmented lesions. The clinical evaluation result was then compared with the two new indices and other published shape measurements. The user study showed that both of the new indices vastly outperformed the other shape descriptors. Moreover, our algorithm captured the knowledge of expert dermatologists in analysing malignancy of a lesion based on its shape alone, indicating that the new measures may be useful for diagnosing melanomas.

A novel method for detection of pigment network in dermoscopic images using graphs.

We describe a novel approach to detect and visualize pigment network structures in dermoscopic images, based on the fact that the edges of pigment network structures form cyclic graphs which can be automatically detected and analyzed. First we perform a pre-processing step of image enhancement and edge detection. The resulting binary edge image is converted to a graph and the defined feature patterns are extracted by finding cyclic subgraphs corresponding to skin texture structures. We filtered these cyclic subgraphs to remove other round structures such as globules, dots, and oil bubbles, based on their size and color. Another high-level graph is created from each correctly extracted subgraph, with a node corresponding to a hole in the pigment network. Nodes are connected by edges according to their distances. Finally the image is classified according to the density ratio of the graph. Our results over a set of 500 images from a well known atlas of dermoscopy show an accuracy of 94.3% on classification of the images as pigment network Present or Absent.

Combining 2D and 3D views for orientation and relative position tasks

We compare 2D/3D combination displays to displays with 2D and 3D views alone. Combination displays we consider are: orientation icon (i.e., side-by-side), in-place methods (e.g., clip planes), and a new method called ExoVis. We specifically analyze performance differences (i.e., time and accuracy) for 3D orientation and relative position tasks. Empirical results show that 3D displays are effective for approximate navigation and relative positioning whereas 2D/3D combination displays (orientation icon and ExoVis) are useful for precise orientation and position tasks. Combination 2D/3D displays had as good or better performance as 2D displays. Clip planes were not effective for a 3D orientation task, but may be useful when only one slice is needed.

A Fully Automatic Random Walker Segmentation for Skin Lesions in a Supervised Setting

We present a method for automatically segmenting skin lesions by initializing the random walker algorithm with seed points whose properties, such as colour and texture, have been learnt via a training set. We leverage the speed and robustness of the random walker algorithm and augment it into a fully automatic method by using supervised statistical pattern recognition techniques. We validate our results by comparing the resulting segmentations to the manual segmentations of an expert over 120 cases, including 100 cases which are categorized as difficult (i.e.: low contrast, heavily occluded, etc.). We achieve an F-measure of 0.95 when segmenting easy cases, and an F-measure of 0.85 when segmenting difficult cases.

Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel

The main challenge in an automated diagnostic system for the early diagnosis of melanoma is the correct segmentation and classification of moles, often occluded by hair in images obtained with a dermoscope. Hair occlusion causes segmentation algorithms to fail to identify the correct nevus border, and can cause errors in estimating texture measures. We present a new method to identify hair in dermoscopic images using a universal approach, which can segment both dark and light hair without prior knowledge of the hair type. First, the hair is amplified using a universal matched filtering kernel, which generates strong responses for both dark and light hair without prejudice. Then we apply local entropy thresholding on the response to get a raw binary hair mask. This hair mask is then refined and verified by a model checker. The model checker includes a combination of image processing (morphological thinning and label propagation) and mathematical (Gaussian curve fitting) techniques. The result is a clean hair mask which can be used to segment and disocclude the hair in the image, preparing it for further segmentation and analysis. Application on real dermoscopic images yields good results for thick hair of varying colours, from light to dark. The algorithm also performs well on skin images with a mixture of both dark and light hair, which was not previously possible with previous hair segmentation algorithms.

What Do Surgeons See: Capturing and Synchronizing Eye Gaze for Surgery Applications

Recording eye motions in surgical environments is challenging. This study describes the authors’ experiences with performing eye-tracking for improving surgery training, both in the laboratory and in the operating room (OR). Three different eye-trackers were used, each with different capabilities and requirements. For monitoring eye gaze shifts over the room scene in a simulated OR, a head-mounted system was used. The number of surgeons’ eye glances on the monitor displaying patient vital signs was successfully captured by this system. The resolution of the head-mounted eye-tracker was not sufficient to obtain the gaze coordinates in detail on the surgical display monitor. The authors then selected a high-resolution eye-tracker built in to a 17-inch computer monitor that is capable of recording gaze differences with resolution of 1° of visual angle. This system enables one to investigate surgeons’ eye–hand coordination on the surgical monitor in the laboratory environment. However, the limited effective tracking distance restricts the use of this system in the dynamic environment in the real OR. Another eye-tracker system was found with equally high level of resolution but with more flexibility on the tracking distance, as the eye-tracker camera was detached from the monitor. With this system, the surgeon’s gaze during 11 laparoscopic procedures in the OR was recorded successfully. There were many logistical challenges with unobtrusively integrating the eye-tracking equipment into the regular OR workflow and data processing issues in the form of image compatibility and data validation. The experiences and solutions to these challenges are discussed.

Dermascopic hair disocclusion using inpainting

Inpainting, a technique originally used to restore film and photographs, is used to disocclude hair from dermascopic images of skin lesions. The technique is compared to the conventional software DullRazor, which uses linear interpolation to perform disocclusion. Comparison was performed by simulating occluding hair on a dermascopic image, applying DullRazor and inpainting and calculating the error induced. Inpainting is found to perform approximately 33% better than DullRazors linear interpolation, and is more stable under heavy occlusion. The results are also compared to published results from two other alternatives: auto-regressive (AR) model signal extrapolation and band-limited (BL) signal interpolation.

Improving the Utility of Speech Recognition Through Error Detection

Despite the potential to dominate radiology reporting, current speech recognition technology is thus far a weak and inconsistent alternative to traditional human transcription. This is attributable to poor accuracy rates, in spite of vendor claims, and the wasted resources that go into correcting erroneous reports. A solution to this problem is post-speech-recognition error detection that will assist the radiologist in proofreading more efficiently. In this paper, we present a statistical method for error detection that can be applied after transcription. The results are encouraging, showing an error detection rate as high as 96% in some cases.