Biomedical subjects

R Joe Stanley

Publications and source records attributed to R Joe Stanley.

9 recordsLinked to original sources

PubMed2005

Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes.

BACKGROUND: Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at x 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin. METHODS: In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated. RESULTS: Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist. CONCLUSIONS: The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images.

Algorithms↗

PubMed2005

A systematic heuristic approach for feature selection for melanoma discrimination using clinical images.

BACKGROUND: Numerous features are derived from the asymmetry, border irregularity, color variegation, and diameter of the skin lesion in dermatology for diagnosing malignant melanoma. Feature selection for the development of automated skin lesion discrimination systems is an important consideration. METHODS: In this research, a systematic heuristic approach is investigated for feature selection and lesion classification. The approach integrates statistical-, correlation-, histogram-, and expert system-based components. Using statistical and correlation measures, interrelationships among features are determined. Expert system analysis is performed to identify redundant features. The feature selection process is applied to 19 shape and color features for a clinical image data set containing 355 malignant melanomas, 125 basal cell carcinomas, 177 dysplastic nevi, 199 nevocellular nevi, 139 seborrheic keratoses, and 45 vascular lesions. RESULTS: Experimental results show reduced lesion classification error rates based on condensing the shape and color feature set from 19 features to 13 features using the feature selection process. Specifically, average test lesion classification error rates for discriminating malignant melanoma from non-melanoma lesions were reduced from 26.6% for 19 features to 23.2% for 13 features over five randomly generated training and test sets. CONCLUSIONS: The experimental results show that the systematic heuristic approach for feature reduction can be successfully applied to achieve improved lesion discrimination. The feature reduction technique facilitates the elimination of redundant information that may inhibit lesion classification performance. The clinical application of this result is that automated skin lesion classification algorithm development can be fostered with systematic feature selection techniques.

Basal Cell Carcinoma↗

PubMed2005

Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color.

BACKGROUND: Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area). METHOD: Using both relative and absolute colors, blotches are detected in this research automatically by using thresholds in the red and green color planes. Several blotch indices are computed, including the scaled distance between the largest blotch centroid and the lesion centroid, ratio of total blotch areas to lesion area, ratio of largest blotch area to lesion area, total number of blotches, size of largest blotch, and irregularity of largest blotch. RESULTS: The effectiveness of the absolute and relative color blotch features was examined for melanoma/benign lesion discrimination over a dermoscopy image set containing 165 melanomas (151 invasive melanomas and 14 melanomas in situ) and 347 benign lesions (124 nevocellular nevi without dysplasia and 223 dysplastic nevi) using a leave-one-out neural network approach. Receiver operating characteristic curve results are shown, highlighting the sensitivity and specificity of melanoma detection. Statistical analysis of the blotch features are also presented. CONCLUSION: Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%.

Color↗

PubMed2003

Colour analysis of skin lesion regions for melanoma discrimination in clinical images.

BACKGROUND: Skin lesion colour is an important feature for diagnosing malignant melanoma. Colour histogram analysis over a training set of images has been used to identify colours characteristic of melanoma, i.e., melanoma colours. A percent melanoma colour feature defined as the percentage of the lesion pixels that are melanoma colours has been used as a feature to discriminate melanomas from benign lesions. METHODS: In this research, the colour histogram analysis technique is extended to evaluate skin lesion discrimination based on colour feature calculations in different regions of the skin lesion. The colour features examined include percent melanoma colour and a novel colour clustering ratio. Experiments are performed using clinical images of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi. RESULTS: Experimental results show improved discrimination capability for feature calculations focused in the lesion boundary region. Specifically, correct melanoma and benign recognition rates are observed as high as 89 and 83%, respectively, for the percent melanoma colour feature computed using only the outermost, uniformly distributed 10% of the lesion's area. CONCLUSIONS: The experimental results show for the features investigated that the region closest to the skin lesion boundary contains the greatest colour discrimination information for lesion screening. Furthermore, the percent melanoma colour feature consistently outperformed the colour clustering ratio for the different skin lesion regions examined. The clinical application of this result is that clustered colours appear to be no more significant than colours of arbitrary distribution within a lesion.

Colorimetry↗

PubMed2003

Colour histogram analysis for melanoma discrimination in clinical images.

BACKGROUND: Malignant melanoma, the most deadly form of skin cancer, has a good prognosis if treated in the curable early stages. Colour provides critical discriminating information for the diagnosis of malignant melanoma. METHODS: This research introduces a three-dimensional relative colour histogram analysis technique to identify colours characteristic of melanomas and then applies these 'melanoma colours' to differentiate benign skin lesions from melanomas. The relative colour of a skin lesion is determined based on subtracting a representative colour of the surrounding skin from each lesion pixel. A colour mapping for 'melanoma colours' is determined using a training set of images. A percent melanoma colour feature, defined as the percentage of the lesion pixels that are melanoma colours, is used for discriminating melanomas from benign lesions. The technique is evaluated using a clinical image data set of 129 malignant melanomas and 129 benign lesions consisting of 40 seborrheic keratoses and 89 nevocellular nevi. RESULTS: Using the percent melanoma colour feature for discrimination, experimental results yield correct melanoma and benign lesion discrimination rates of 84.3 and 83.0%, respectively. CONCLUSIONS: The results presented in this work suggest that lesion colour in clinical images is strongly related to the presence of melanoma in that lesion. However, colour information should be combined with other information in order to further reduce the false negative and false positive rates.

Algorithms↗

PubMed2002

An integrated pattern recognition approach for intrusion detection.

Intrusion detection systems (IDS) attempt to address the vulnerability of computer-based systems for abuse by insiders and to penetration by outsiders. An IDS is required to examine an enormous amount of data generated by computer networks to assist in the abuse detection process. Thus, there is a need to develop automated tools that address these requirements to assist system operators in the detection of violations of existing security policies. In this research, an automated IDS is proposed for insider threats in a distributed system. The proposed IDS functions as an anomaly detector for insider system operations based on the analysis of the system's log files. The approach integrates dynamic programming and adaptive resonance theory (ART1) clustering. The integrated approach aligns sequences of log events with prototypical sequences of events for performing tasks and classifies the aligned sequences for intrusion detection. The system examined for this research is a Boots System for controlling the movement of boots from one place to another under specific security restrictions related to the boot orders. We present the proposed model, the results achieved and the analysis of an implemented prototype.

Algorithms↗

PubMedDate not supplied

Anterior osteophyte discrimination in lumbar vertebrae using size-invariant features.

Radiologists often examine X-rays of cervical, thoracic and lumbar vertebrae for determining the presence of osteoarthritis and osteoporosis. For individual vertebra assessment, the boundary increasingly digresses from the general rectangular shape as the vertebra becomes less normal in appearance. For an abnormal vertebra, bony growths ('osteophytes') may appear at the vertebral comers, resulting in a change in the vertebra's shape. Image processing techniques are presented for computing size-invariant, convex hull-based features to highlight anterior osteophytes. Feature evaluation of 714 lumbar spine vertebrae using a multi-layer perceptron yielded normal and abnormal average correct discrimination of 90.5 and 86.6%, respectively.

Algorithms↗

PubMedDate not supplied

Image analysis techniques for characterizing disc space narrowing in cervical vertebrae interfaces.

Image analysis techniques are introduced for evaluating disc space narrowing of cervical vertebrae interfaces from X-ray images. Four scale-invariant, distance transform-based features are presented for characterizing the spacing between adjacent vertebrae. K-means and self-organizing map clustering techniques are applied to estimate the degree of disc space narrowing using a four grade (0-3) scoring system, where 0 and 3 represent normal spacing and significant narrowing, respectively. For a data set of 294 vertebrae interfaces, experimental results yield average correct grade assignment of greater than 82.10% for each of the four grades using a one grade window around the correct grade.

Algorithms↗

PubMedDate not supplied

A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images.

A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermatology clinical images. The approach utilizes a fuzzy set for benign skin lesion color, and alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy ratio and fusion with a previously determined percent melanoma color feature over a data set of 258 clinical images. For the fusion technique, alpha-cuts for the fuzzy ratio can be chosen to recognize over 93.30% of melanomas with approximately 15.67% false positive lesions.

Algorithms↗