Detecting field cancerization using a hyperspectral imaging system

Comprehensive performance domain tolerance analysis methodology for freeform imaging spectrometers

In recent years, attention has been directed towards cost-effective and compact freeform Schwarzschild imaging spectrometers with plane gratings. The utilization of tolerance analysis serves as a potent approach to facilitate the development of prototypes. Conventional tolerance analysis methods often rely solely on the modulation transfer function (MTF) criterion. However, for a spectrometer system, factors such as the keystone/smile distortion and spectral resolution performance also require consideration. In this study, a tailored comprehensive performance domain tolerance analysis methodology for freeform imaging spectrometers was developed, considering vital aspects such as the MTF, keystone/smile distortion, and spectral resolution. Through this approach, meticulous tolerance analysis was conducted for a freeform Schwarzschild imaging spectrometer, providing valuable insights for the prototype machining and assembly processes. Emphasis was placed on the necessity of precise control over the tilt and decenter between the first and third mirrors, whereas the other fabrication and assembly tolerances adhered to the standard requirements. Finally, an alignment computer-generated hologram (CGH) was employed for the preassembly of the first and third mirrors, enabling successful prototype development. The congruence observed between the measured results and tolerance analysis outcomes demonstrates the effectiveness of the proposed method.

Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues

Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre-cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro-inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options.

Hyperspectral imaging to accurately segment skin erythema and hyperpigmentation in cutaneous chronic graft-versus-host disease

In 51 lesions from 15 patients with the inflammatory skin condition chronic graft-versus-host-disease, hyperspectral imaging accurately delineated active erythema and post-inflammatory hyperpigmentation. The method was validated by dermatologist-approved confident delineations of only definitely affected and definitely unaffected areas in photographs. A prototype hyperspectral imaging system acquired a 2.5 × 3.5 cm2 area of skin at 120 wavelengths in the 450-850 nm range. Unsupervised extraction of unknown absorbers by endmember analysis achieved a comparable accuracy to that of supervised extraction of known absorbers (melanin, hemoglobin) by chromophore mapping: 0.78 (IQR: 0.39-0.85) vs. 0.83 (0.53-0.91) to delineate erythema and 0.74 (0.57-0.87) vs. 0.73 (0.52-0.84) to delineate hyperpigmentation. Both algorithms achieved higher specificity than sensitivity. Whereas a trained human confidently marked a median of 7% of image pixels, unsupervised and supervised algorithms delineated a median of 14% and 27% pixels. Hyperspectral imaging could overcome a fundamental practice gap of distinguishing active from inactive manifestations of inflammatory skin disease.

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging

Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible.

Classification of emotional stress and physical stress using a multispectral based deep feature extraction model

A classification model (Stress Classification-Net) of emotional stress and physical stress is proposed, which can extract classification features based on multispectral and tissue blood oxygen saturation (StO2) characteristics. Related features are extracted on this basis, and the learning model with frequency domain and signal amplification is proposed for the first time. Given that multispectral imaging signals are time series data, time series StO2 is extracted from spectral signals. The proper region of interest (ROI) is obtained by a composite criterion, and the ROI source is determined by the universality and robustness of the signal. The frequency-domain signals of ROI are further obtained by wavelet transform. To fully utilize the frequency-domain characteristics, the multi-neighbor vector of locally aggregated descriptors (MN-VLAD) model is proposed to extract useful features. The acquired time series features are finally put into the long short-term memory (LSTM) model to learn the classification characteristics. Through SC-NET model, the classification signals of emotional stress and physical stress are successfully obtained. Experiments show that the classification result is encouraging, and the accuracy of the proposed algorithm is over 90%.

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

SIGNIFICANCE

Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.

AIM

We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.

APPROACH

A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.

RESULTS

HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.

CONCLUSIONS

To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours-A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477-891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces.

A review of the medical hyperspectral imaging systems and unmixing algorithms' in biological tissues

Hyperspectral fluorescence imaging (HFI) is a well-known technique in the medical research field and is considered a non-invasive tool for tissue diagnosis. This review article gives a brief introduction to acquisition methods, including the image preprocessing methods, feature selection and extraction methods, data classification techniques and medical image analysis along with recent relevant references. The process of fusion of unsupervised unmixing techniques with other classification methods, like the combination of support vector machine with an artificial neural network, the latest snapshot Hyperspectral imaging (HSI) and vortex analysis techniques are also outlined. Finally, the recent applications of hyperspectral images in cellular differentiation of various types of cancer are discussed.

Hyperspectral imaging for diagnosis and detection of ex-vivo breast cancer

BACKGROUND AND PURPOSE

Breast cancer is one of the most widely recognized tumors. .Diagnosis made in the early stage of disease may imporve outcomes. The discovery of malignant growth utilizing noninvasive light intrusive methods in lieu of conventional excisional biopsy may assist in achieving this goal.

MATERIALS AND METHODS

The change of the optical properties of ex-vivo breast tissues provides different responses to light transmission, absorption, and particularly the reflection over the spectrum range. We offer the use of Hyperspectral imaging (HSI) with advanced image processing and pattern recognition in order to analyze HSI data for breast cancer detection. The spectral signatures were mined and evaluated in both malignant and normal tissue. K-mean clustering was designed for classifying hyperspectral data in order to evaluate and detection of cancer tissue. This method was used to detect ex-vivo breast cancer. Spatial spectral images were created to high spot the differences in the reflectance properties of malignant versus normal tissue.

RESULTS

Trials showed that the superficial spectral reflection images within 500 nm wavelength showed high variance (214.65) between cancerous and normal breast tissues. On the other hand, image within 620 nm wavelength showed low variance (0.0020).However, the superimposed of spectral region 420-620 nm was proposed as the optimum bandwidth. Finally, the proposed HS imaging system was capable to discriminate the tumor region from normal tissue of the ex-vivo breast sample with sensitivity and a specificity of 95 % and 96 %.

CONCLUSIONS

High sensitivity and specificity were achieved, which proposes potential for HSI as an edge evaluation method to enhance the surgical outcome compared to the presently available techniques in the clinics.

Actinic keratosis and surrounding skin exhibit changes in corneocyte surface topography and decreased levels of filaggrin degradation products

Actinic keratosis (AK) is a frequent premalignant skin lesion mainly caused by chronic sun exposure. AK lesions are often surrounded by invisible, subclinical alterations, called field of cancerization (FoC). Definition of FoC is of importance for therapy management; however, the criteria and non-invasive tools to characterize FoC are lacking. Atomic force microscopy (AFM) proved to be a suitable tool for detection of changes in the corneocyte surface topography in inflammatory skin diseases, which share similar clinical features with AK such as hyper- and parakeratosis. Therefore, in this study we applied AFM to investigate AK and surrounding skin obtained by non-invasive collection of the stratum corneum (SC) with adhesive tapes. Furthermore, we determined degradation products of structural protein filaggrin (natural moisturizing factor, NMF), which previously showed association with the changes in corneocyte surface topography. Ten patients with multiple AK on the face were recruited from the outpatient clinic. SC samples were collected from the AK lesion, skin sites adjacent to the AK, 5 cm from the AK and retroauricular area. Corneocyte surface topography was determined by AFM, and NMF by liquid chromatography. The AK lesion showed alterations of the corneocyte surface topography characterized by an increased number of nanosize protrusions, which gradually decreased with the distance from the lesion. NMF levels show an inverse pattern. Atomic force microscopy showed to be a suitable tool to detect changes in the corneocyte surface topography on the AK lesion and surrounding skin in a non-invasive manner.

Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650 nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome.

Intra-operative point-of-procedure delineation of oral cancer margins using optical coherence tomography

OBJECTIVES

Surgical margin status is a significant determinant of treatment outcome in oral cancer. Negative surgical margins can decrease the loco-regional recurrence by five-fold. The current standard of care of intraoperative clinical examination supplemented by histological frozen section, can result in a risk of positive margins from 5 to 17 percent. In this study, we attempted to assess the utility of intraoperative optical coherence tomography (OCT) imaging with automated diagnostic algorithm to improve on the current method of clinical evaluation of surgical margin in oral cancer.

MATERIALS AND METHODS

We have used a modified handheld OCT device with automated algorithm based diagnostic platform for imaging. Intraoperatively, images of 125 sites were captured from multiple zones around the tumor of oral cancer patients (n = 14) and compared with the clinical and pathologic diagnosis.

RESULTS

OCT showed sensitivity and specificity of 100%, equivalent to histological diagnosis (kappa, ĸ = 0.922), in detection of malignancy within tumor and tumor margin areas. In comparison, for dysplastic lesions, OCT-based detection showed a sensitivity of 92.5% and specificity of 68.8% and a moderate concordance with histopathology diagnosis (ĸ = 0.59). Additionally, the OCT scores could significantly differentiate squamous cell carcinoma (SCC) from dysplastic lesions (mild/moderate/severe; p ≤ 0.005) as well as the latter from the non-dysplastic lesions (p ≤ 0.05).

CONCLUSION

The current challenges associated with clinical examination-based margin assessment could be improved with intra-operative OCT imaging. OCT is capable of identifying microscopic tumor at the surgical margins and demonstrated the feasibility of mapping of field cancerization around the tumor.

On the spectral signature of melanoma: a non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions

Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.

Hyperspectral imaging system in the delineation of Ill-defined basal cell carcinomas: a pilot study

BACKGROUND

Basal cell carcinoma (BCC) is the most common skin cancer in the Caucasian population. Eighty per cent of BCCs are located on the head and neck area. Clinically ill-defined BCCs often represent histologically aggressive subtypes, and they can have subtle subclinical extensions leading to recurrence and the need for re-excisions.

OBJECTIVES

The aim of this pilot study was to test the feasibility of a hyperspectral imaging system (HIS) in vivo in delineating the preoperatively lateral margins of ill-defined BCCs on the head and neck area.

METHODS

Ill-defined BCCs were assessed clinically with a dermatoscope, photographed and imaged with HIS. This was followed by surgical procedures where the BCCs were excised at the clinical border and the marginal strip separately. HIS, with a 12-cm² field of view and fast data processing, records a hyperspectral graph for every pixel in the imaged area, thus creating a data cube. With automated computational modelling, the spectral data are converted into localization maps showing the tumour borders. Interpretation of these maps was compared to the histologically verified tumour borders.

RESULTS

Sixteen BCCs were included. Of these cases, 10 of 16 were the aggressive subtype of BCC and 6 of 16 were nodular, superficial or a mixed type. HIS delineated the lesions more accurately in 12 of 16 of the BCCs compared to the clinical evaluation (4 of 16 wider and 8 of 16 smaller by HIS). In 2 of 16 cases, the HIS-delineated lesion was wider without histopathological confirmation. In 2 of 16 cases, HIS did not detect the histopathologically confirmed subclinical extension.

CONCLUSIONS

HIS has the potential to be an easy and fast aid in the preoperative delineation of ill-defined BCCs, but further adjustment and larger studies are warranted for an optimal outcome.

Defining the actinic keratosis field: a literature review and discussion

Despite the chronic and increasingly prevalent nature of actinic keratosis (AK) and existing evidence supporting assessment of the entire cancerization field during clinical management, a standardized definition of the AK field to aid in the understanding and characterization of the disease is lacking. The objective of this review was to present and appraise the available evidence describing the AK cancerization field, with the aim of determining a precise definition of the AK field in terms of its molecular (including genetic and immunological), histological and clinical characteristics. Eight European dermatologists collaborated to conduct a review and expert appraisal of articles detailing the characteristics of the AK field. Articles published in English before August 2016 were identified using PubMed and independently selected for further assessment according to predefined preliminary inclusion and exclusion criteria. In addition, a retrospective audit of patients with AK was performed to define the AK field in clinical terms. A total of 32 review articles and 47 original research articles provided evidence of sun-induced molecular (including genetic and immunological) and histological skin changes in the sun-exposed area affected by AK. However, the available literature was deemed insufficient to inform a clinical definition of the AK field. During the retrospective audit, visible signs of sun damage in 40 patients with AK were assessed. Telangiectasia, atrophy and pigmentation disorders emerged as 'reliable or very reliable' indicators of AK field based on expert opinion, whereas 'sand paper' was deemed a 'moderately reliable' indicator. This literature review has revealed a significant gap of evidence to inform a clinical definition of the AK field. Therefore, the authors instead propose a clinical definition of field cancerization based on the identification of visible signs of sun damage that are reliable indicators of field cancerization based on expert opinion.

Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People's Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874-1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.

Combined reflectance confocal microscopy-optical coherence tomography for delineation of basal cell carcinoma margins: an ex vivo study

We present a combined reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) approach, integrated within a single optical layout, for diagnosis of basal cell carcinomas (BCCs) and delineation of margins. While RCM imaging detects BCC presence (diagnoses) and its lateral spreading (margins) with measured resolution of ∼1  μm, OCT imaging delineates BCC depth spreading (margins) with resolution of ∼7  μm. When delineating margins in 20 specimens of superficial and nodular BCCs, depth could be reliably determined down to ∼600  μm, and agreement with histology was within about ±50  μm.

Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging

This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023 nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715 nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves.

Hyperspectral imaging in medicine: image pre-processing problems and solutions in Matlab

The paper presents problems and solutions related to hyperspectral image pre-processing. New methods of preliminary image analysis are proposed. The paper shows problems occurring in Matlab when trying to analyse this type of images. Moreover, new methods are discussed which provide the source code in Matlab that can be used in practice without any licensing restrictions. The proposed application and sample result of hyperspectral image analysis.