High-sensitivity electronic Stark spectrometer featuring a laser-driven light source.

We report developmental details of a high-sensitivity Stark absorption spectrometer featuring a laser-driven light source. The light source exhibits intensity fluctuations of ∼0.3% over timescales ranging from 1 min to 12 h, minimal drift (≤0.1%/h), and very little 1/f noise at frequencies greater than 200 Hz, which are comparable to or better than an arc-driven light source. Additional features of the spectrometer include balanced detection with multiplex sampling, which yielded lower noise in A, and constant wavelength or wavenumber (energy) spectral bandpass modes. We achieve noise amplitudes of ∼7 × 10-4 and ∼6 × 10-6 in measurements of single A and ΔA spectra (with 92 data points) taking ∼7 and ∼19 min, respectively.

A deep learning approach to detect blood vessels in basal cell carcinoma.

PURPOSE: Blood vessels called telangiectasia are visible in skin lesions with the aid of dermoscopy. Telangiectasia are a pivotal identifying feature of basal cell carcinoma. These vessels appear thready, serpiginous, and may also appear arborizing, that is, wide vessels branch into successively thinner vessels. Due to these intricacies, their detection is not an easy task, neither with manual annotation nor with computerized techniques. In this study, we automate the segmentation of telangiectasia in dermoscopic images with a deep learning U-Net approach. METHODS: We apply a combination of image processing techniques and a deep learning-based U-Net approach to detect telangiectasia in digital basal cell carcinoma skin cancer images. We compare loss functions and optimize the performance by using a combination loss function to manage class imbalance of skin versus vessel pixels. RESULTS: We establish a baseline method for pixel-based telangiectasia detection in skin cancer lesion images. An analysis and comparison for human observer variability in annotation is also presented. CONCLUSION: Our approach yields Jaccard score within the variation of human observers as it addresses a new aspect of the rapidly evolving field of deep learning: automatic identification of cancer-specific structures. Further application of DL techniques to detect dermoscopic structures and handle noisy labels is warranted.

Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images.

PURPOSE: Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation. METHODS: Fifteen thresholding methods were implemented for BCC lesion segmentation. We propose two error metrics that better measure the type II error: Relative XOR Error and Lesion Capture Ratio. RESULTS: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics. CONCLUSION: The proposed algorithms, which include solutions for image vignetting correction and border expansion to achieve dermatologist-like borders, provide more inclusive and feature-preserving border detection, favoring better BCC classification accuracy, in future work.

Automatic telangiectasia analysis in dermoscopy images using adaptive critic design.

BACKGROUND: Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs. METHODS: A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply action-dependent heuristic dynamic programming (ADHDP) for discrimination based on computed features using different skin lesion contrast variations to promote the discrimination process. Lesion discrimination results for ADHDP are compared with multilayer perception backpropagation artificial neural networks. RESULTS: This study uses a data set of 498 dermoscopy skin lesion images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the ADHDP approach, providing an 8.03% improvement over a standard multilayer perception method. CONCLUSION: We have chosen BCC detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, BCC detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research.

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images.

Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.

Microarray image spot segmentation using the method of projections.

Microarray technology is increasingly used as a means of high throughput analysis of human, non-human and plant genomes. Manual methods of array production using this technology lead to many inherent problems in the microarray image produced. The density of the spots in the images produced is very high, such that neighboring spots can overlap. The image background is often not uniform, containing noise that is often difficult to distinguish from actual spots. In this research, a projections-based approach is investigated for spot segmentation in paired radio probe microarray images. An important aspect of spot segmentation is the capability to perform corresponding spot-to-spot comparisons between the paired images. Experimental results are presented for spot segmentation in isolated and paired microarray images.

Evidence of powerful substrate electric fields in DNA photolyase: implications for thymidine dimer repair.

DNA photolyase is a flavoprotein that repairs cyclobutylpyrimidine dimers by ultrafast photoinduced electron transfer. One unusual feature of this enzyme is the configuration of the FAD cofactor, where the isoalloxazine and adenine rings are nearly in vdW contact. We have measured the steady-state and transient absorption spectra and excited-state decay kinetics of oxidized (FAD-containing, folate-depleted) Escherichia coli DNA photolyase with and without dinucleotide and polynucleotide single-stranded thymidine dimer substrates. The steady-state absorption spectrum for the enzyme-polynucleotide substrate complex showed a blue shift, as seen previously by Jorns et al. (1). No shift was observed for the dinucleotide substrate, suggesting that there are significant differences in the binding geometry of dinucleotide versus polynucleotide dimer lesions. Evidence was obtained from transient absorption experiments for a long-lived charge-transfer complex involving the isoalloxazine of the FAD cofactor. No evidence of excited-state quenching was measurable upon binding either substrate. To explain these data, we hypothesize the existence of a large substrate electric field in the cavity containing the FAD cofactor. A calculation of the magnitude and direction of this dipolar electric field is consistent with electrochromic band shifts for both S(0) --> S(1) and S(0) --> S(2) transitions. These observations suggest that the substrate dipolar electric field may be a critical component in its electron-transfer-mediated repair by photolyase and that the unique relative orientation of the isoalloxazine and adenine rings may have resulted from the consequences of the dipolar substrate field.

A radius of curvature-based approach to cervical spine vertebra image analysis.

Radiologists interpret the normality of vertebras within cervical spine x-rays for determining the presence of osteoporosis. Features relating to vertebra fracture such as contrasting anterior and posterior heights are used in the vertebra normality assessment. Vertebra distortion along the anterior boundary can be used as an indicator of vertebra normality. As the vertebra becomes less normal in appearance, the vertebra boundary increasingly deviates from the general rectangular shape. This research introduces a radius of curvature-based approach to quantify the distortion in vertebra boundary shape for anterior osteophytes classification of vertebras within cervical spine x-ray images. An adaptive histogram analysis technique is used to enhance the vertebras within cervical spine x-ray images to facilitate vertebra feature extraction. A least squares technique is used for calculating the radii of curvature along the vertebra boundary. Radius of curvature and gray-level based features are computed using the anterior boundary point position with the minimum radius of curvature as the reference. A total of 118 cervical spine vertebras are used for training and testing various classifiers including a standard back propagation neural network, K-Means algorithm, quadratic discriminant classifier and LVQ3. The results from those classifiers are reported for recognizing vertebras as normal or abnormal.

Advances in flavin and flavoprotein optical spectroscopy.

Flavins and flavoproteins are versatile redox cofactors that can perform both one- and two-electron transfer. Because they are highly colored in all three oxidation states, optical spectroscopy has been exploited for decades to study these redox changes. This review summarizes the application of optical spectroscopies to flavins and flavoproteins since 1990. Special emphasis is placed on new techniques, such as Stark spectroscopy, as well as significant refinements in more well known techniques, such as resonance Raman spectroscopy and ultrafast spectroscopy.

Data-driven homologue matching for chromosome identification.

Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the development of image analysis techniques which are extendible to detecting numerical aberrations evolving from structural abnormalities. Specifically, an approach to identifying "normal" chromosomes from selected class(es) within a metaphase spread is presented. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to our homologue matcher for identifying class 17 within normal and abnormal metaphase spreads.

Homologue matching using the Choquet integral.

Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes, 1-22, and X chromosome for females. For optimizing automated human chromosome image analysis, many existing techniques assume cell normalcy. With many genetic abnormalities directly linked to structural and numerical aberrations of chromosomes within the metaphase spread, the two chromosome per class assumption may not be appropriate for anomaly analysis. At the University of Missouri, a data-driven homologue matching approach has been developed to identify all normal chromosomes within a metaphase spread from a selected class. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching utilizing a density profile-based classifier, a shape profile-based classifier, and a binary band profile-based classifier. Based on preliminary results for the profile-based classifiers assigning chromosome 17, the Choquet integral is presented as an extension to the homologue matching approach. Experimental results are presented comparing the extended homologue matching approach to the transportation algorithm for identifying chromosome 21 within normal metaphase spreads.

Two-photon excitation of 4'-hydroxymethyl-4,5',8-trimethylpsoralen.

Psoralens are a class of pharmaceutical agents commonly used to treat several cutaneous disorders. When irradiated with a mode-locked titanium: sapphire (Ti:sapphire) laser tuned to 730 nm, an aqueous solution of 4'-hydroxymethyl-4,5',8-trimethylpsoralen (HMT) emits blue light. The emission spectrum is centered at 452 nm and is identical to that obtained by one-photon excitation with UVA excitation, and its magnitude depends quadratically on the intensity of laser excitation. These results suggest that two-photon excitation occurs to a potentially photochemically active state. To estimate the two-photon absorption cross section, it was first necessary to measure the emission quantum yield of HMT using 365 nm excitation at room temperature that resulted in a value of 0.045 +/- 0.007. The two-photon absorption cross section of HMT at 730 nm is therefore estimated to be 20 x 10(-50) cm4 s (20 Göppert-Mayer). The excited-state photophysics and photochemistry of psoralens suggest potential applications to cutaneous phototherapy in diseases such as psoriasis and dystrophic epidermolysis bullosa.

The effects of image manipulation on automated karyotyping.

Karyotyping is one of the standard tools for human genetic investigations. Karyotyping involves the classification and interpretation of chromosomes found in a metaphase spread. As part of the automated karyotyping process, image manipulation is required for appropriately positioning metaphase spread chromosomes in their corresponding karyotype. Image manipulation, specifically image rotation, reorganizes the grey-level information within chromosomes to facilitate analysis. Statistical tests are performed to compare features related to banding pattern and length between unmanipulated chromosomes and corresponding rotated chromosomes. Based on experimental results, reorganizing the grey-level information yields statistically different chromosome features. Depending on the purpose of chromosome image analysis, the interpretation process of karyotyping could be impaired with chromosome feature distortion due to image rotation.

Endoscopic therapy for organized pancreatic necrosis.

BACKGROUND & AIMS: The treatment of patients with extensive pancreatic necrosis remains controversial; a subpopulation of patients with extensive acute pancreatic necrosis develop complex, organized collections. This study examined the feasibility of endoscopic drainage in patients with extensive organized pancreatic necrosis. METHODS: Eleven patients with organized pancreatic necrosis (8 sterile and 3 infected) after severe acute necrotizing pancreatitis underwent attempted endoscopic drainage. Dynamic contrast-enhanced computerized tomography showed > or = 50% pancreatic necrosis in 10 of 11 patients in whom endoscopic drainage was performed. In 8 patients, an intrapancreatic nasobiliary lavage catheter was placed into the collection concurrently with 10F stents. RESULTS: Complete resolution was achieved nonoperatively in 9 patients. At a mean follow-up of 12 months, 1 patient developed a pseudocyst, which was successfully drained endoscopically. Complications occurred in 5, including bleeding that precluded entry into one collection. CONCLUSIONS: Endoscopic therapy may be a viable management option for a subset of patients who remain symptomatic after an episode of acute pancreatic necrosis after the necrosis has become organized and partially liquefied. Intrapancreatic lavage catheter placement is essential to the success of this procedure. Further evaluation of this technique is needed before this method can be adopted into clinical practice.

Delayed diagnosis of pancreatic transection after blunt abdominal trauma.

A case of delayed diagnosis of a pancreatic transection after blunt abdominal trauma is presented. The cause of diagnostic delay as well as measures to avoid future errors in diagnosis are outlined.

Centromere attribute integration based chromosome polarity assignment.

Automated karyotyping involves evaluating quantified chromosome attributes for proper classification. Chromosome attributes derived from the banding pattern require the correct chromosome polarity for correct banding sequence interpretation. Chromosome polarity is defined in terms of determining the short and long arms of the chromosome using the centromere as the reference point for measuring the chromosome length on both sides of the centromere. In addition to banding sequence interpretation, polarity is used in the chromosome orientation for chromosome repositioning from the metaphase spread to the karyotype. Automated polarity determination is often not performed for classifying chromosomes in the metaphase spread image. Polarity may be determined user interactively, by the system, or not at all. In order to reduce the computational complexity of evaluating banding sequence features using both chromosome ends as reference points, there is a need to improve chromosome polarity determination in automated karyotyping. A centromere attribute integration approach has been developed at the University of Missouri-Columbia which performs correct chromosome polarity assessment at a rate comparable to other studies of 96.1% on a diversified data set.