Significance and analysis of milia-like cysts in dermoscopy skin lesion images

“Milia-like cysts (MLCs) are dermoscopic structures frequently observed in seborrheic keratoses(SKs), which are the most common type of skin lesions. Diverse appearances of these skin lesions make them difficult to differentiate from melanoma, a deadly type of skin cancer. Classified by size into two main groups, starry MLCs and cloudy MLCs, the presence of these structures in a skin lesion has been known to help differentiate benign lesions from melanoma. Though the presence of cloudy MLCs is not exclusively associated with SKs, they can be a useful tool to differentiate SKs from melanoma. This research study determines the statistical occurrence of MLCs in benign vs. malignant lesions and presents models to differentiate them from the mimics. Various distinct features of these structures such as size, brightness relative to surrounding area, color and shape were used to mark them among the lesions in a training set. A logistic regression model was then used to verify the significant features differentiating these structures from the MLCs and resulted in an area under the receiver operating curve (ROC) of 92.4% for cloudy MLCs and 88.2% for starry MLCs. These models were validated by using a test set”--Abstract, page iv

Identification of starry milia-like cysts in seborrheic keratoses and malignant melanomas using image processing techniques

Seborrheic keratoses are the most common benign tumors found in older individuals. Seborrheic keratoses are normally benign in nature, but sometimes in a large lesion area of seborrheic keratoses, secondary tumors, or squamous cell carcinoma in situ or malignant melanoma may occur. It has been found that seborrheic keratoses have a few features that distinguish them from melanomas. Milia-like cysts, comedo-like openings and network-like structures are the most prominent features found in seborrheic keratoses. Comedo-like openings are pigmented structures and are mostly oval yellowish, brown or black in appearance. They can also be observed in melanocytic lesions and melanomas. Milia-like cysts (MLCs) are structures which are mostly white or yellowish in color, vary in size and are mostly seen in seborrheic keratoses and in certain congenital melanocytic nevi. MLCs are very useful in classifying a skin lesion as a seborrheic keratosis and hence are also useful in differentiating between seborrheic keratoses and melanomas. MLCs are classified into two categories depending on the size and shape of the MLC. The two types of MLCs are starry MLCs and cloudy MLCs. This research project gives a method to automatically identify starry milia-like cysts in any image. The aim of the research is to identify starry milia-like cysts accurately and to distinguish starry MLCs from competing structures like skin pores, bubbles from dermoscopy fluids and keratin scales --Abstract, page iii

Template Matching for Detection of Starry Milia-Like Cysts in Dermoscopic Images

Early detection of melanoma by magnified visible-light imaging (dermoscopy) is hindered by lesions which mimic melanoma. Automatic discrimination of melanoma from mimics could allow detection of melanoma at an earlier stage. Seborrheic keratoses are common mimics; these have distinctive bright structures: starry milia-like cysts (MLCs). We report discrimination of MLCs from mimics by features extracted from starry MLC (star) candidates. After pre-processing, 2D template matching is optimized with respect to star template size, histogram pre-processing, and 2D statistics. The novel aspects of this research were new details for region of interest (ROI) analysis of the centers of the star candidate, a new method for determining shape of hazy objects and multiple template matching, using unprocessed ROIs, shape-limited ROIs, and histogram-equalized ROIs. Features retained in the final model for the decision MLC vs. mimic by logistic regression include star size, 2D first correlation coefficient, correlation coefficient to the star shape template, equalized correlation coefficient, relative star brightness, and statistical features at the star center. These methods allow optimization of MLC features found by 2D template correlation. This research confirms the importance of fine ROI features and ROI neighborhoods in medical imaging