In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer
using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick
texture features from the images and used a feature selection algorithm to identify the most effective feature set for the
diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases.
Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity
we achieved on our data set were 94% and 95%, respectively.
Prostate cancer is the second most common type of cancer among men in US . Traditionally, prostate cancer
diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy
samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging
is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates.
Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample
while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest
confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to
estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a
significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the
high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice.
For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the
most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA
concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools
for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from
prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular
combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are usually identified by
pathologists after examining H&E stained histological microscopy images. Unfortunately, histopathological examination
is currently done on an adjacent slice because the H&E staining process will change tissue's protein structure and it will
derogate MALDI analysis if the same tissue is used, while the MALDI imaging process will destroy the tissue slice so
that it is no longer available for histopathological exam. For this reason, only the most confident cancer region resulting
from the histopathological examination on an adjacent slice will be used to guide the biomarker identification. It is
obvious that a better cancer boundary delimitation on the MALDI imaging slice would be beneficial. In this paper, we
proposed methods to predict the true cancer boundary, using the MALDI MS data, from the most confident cancer region
given by pathologists on an adjacent slice.
Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1.
Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and
prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific
antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a
texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue
sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist as normal tissue or
prostatic carcinoma (PCa). The sample was then subsequently digitized at 50X magnification. We divided the digitized
image into sub-regions of 20 X 20 pixels and classified each sub-region as normal or PCa by a texture analysis method.
In the texture analysis, we computed texture features for each of the sub-regions based on the Gray-level Run-length
Matrix(GL-RLM). Those features include LGRE, HGRE and RPC from the run-length matrix, mean and standard
deviation of the pixel intensity. We utilized a feature selection algorithm to select a set of effective features and used a
multi-layer perceptron (MLP) classifier to distinguish normal from PCa. In total, the whole histological image was
divided into 42 PCa and 6280 normal regions. Three-fold cross validation results show that the proposed method
achieves an average classification accuracy of 89.5% with a sensitivity and specificity of 90.48% and 89.49%,