1
|
Cooney GS, Köhler H, Chalopin C, Babian C. Discrimination of human and animal bloodstains using hyperspectral imaging. Forensic Sci Med Pathol 2024; 20:490-499. [PMID: 37721660 DOI: 10.1007/s12024-023-00689-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2023] [Indexed: 09/19/2023]
Abstract
Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly and sample-consuming tests are performed. To address the question of blood origin, the novel application of visible-near infrared hyperspectral imaging (HSI) is used for the detection and discrimination of human and animal bloodstains. The HSI system used is a portable, non-contact, non-destructive method for the determination of blood origin. A support vector machine (SVM) binary classifier was trained for the discrimination of bloodstains of human (n = 20) and five animal species: pig (n = 20), mouse (n = 16), rat (n = 5), rabbit (n = 5), and cow (n = 20). On an independent test set, the SVM model achieved accuracy, precision, sensitivity, and specificity values of 96, 97, 95, and 96%, respectively. Segmented images of bloodstains aged over a period of two months were produced, allowing for the clear visualisation of the discrimination of human and animal bloodstains. The inclusion of such a system in a forensic investigation workflow not only removes ambiguity surrounding blood origin, but can potentially be used in tandem with HSI bloodstain age determination methods for rapid on-scene forensic analysis.
Collapse
Affiliation(s)
- Gary Sean Cooney
- Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany
| | - Hannes Köhler
- Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany
| | - Claire Chalopin
- Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany
| | - Carsten Babian
- Institute for Legal Medicine, Leipzig University, Leipzig, Germany.
| |
Collapse
|
2
|
Giulietti N, Discepolo S, Castellini P, Martarelli M. Neural network based hyperspectral imaging for substrate independent bloodstain age estimation. Forensic Sci Int 2023; 349:111742. [PMID: 37331047 DOI: 10.1016/j.forsciint.2023.111742] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023]
Abstract
Being able to determine the age of a bloodstain can be a key element in a crime scene investigation. Many techniques exploit reflectance spectroscopy because it is very versatile and can be used in the field with ease. However, there are no methods for estimating bloodstain age with adequate uncertainty, and the problem of substrate influence is not yet fully resolved. We develop a hyperspectral imaging based technique for the substrate-independent age estimation of a bloodstain. Once the hyperspectral image is acquired, a neural network model recognizes the pixels belonging to the bloodstain. The reflectance spectra belonging to the bloodstain are then processed by an artificial intelligence model that removes the effect of the substrate on the bloodstain and then estimates its age. The method is trained on bloodstains deposited on 9 different substrates over a time period of 0-385 h obtaining an absolute mean error of 6.9 h over the period considered. Within two days of age, the method achieves a mean absolute error of 1.1 h. The method is finally tested on a new material (i.e., red cardboard) never used to test or validate the neural network models. Also in this case the bloodstain age is identified with the same accuracy.
Collapse
Affiliation(s)
- Nicola Giulietti
- Department of Mechanical Engineering, Politecnico di Milano, Via La Masa 1, Milan 60131, Italy.
| | - Silvia Discepolo
- Department of Industrial Engineering and Mathematical Science, Universit'a Politecnica delle Marche, Via Brecce Bianche 12, Ancona 20156, Italy
| | - Paolo Castellini
- Department of Industrial Engineering and Mathematical Science, Universit'a Politecnica delle Marche, Via Brecce Bianche 12, Ancona 20156, Italy
| | - Milena Martarelli
- Department of Industrial Engineering and Mathematical Science, Universit'a Politecnica delle Marche, Via Brecce Bianche 12, Ancona 20156, Italy
| |
Collapse
|
3
|
Cao J, An G, Li J, Wang L, Ren K, Du Q, Yun K, Wang Y, Sun J. Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy. Forensic Sci Res 2023; 8:50-61. [PMID: 37415796 PMCID: PMC10265958 DOI: 10.1093/fsr/owad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 02/10/2023] [Indexed: 07/08/2023] Open
Abstract
Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In the present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague-Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h postcontusion (n = 9). Then, the samples were analysed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4, 8, 12, 16-20, 24-32, 36-40, and 44-48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6%, which was much higher than that of the single model. In summary, the multilayer perceptron-multilayer perceptron tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework. Key Points The changes of metabolite profile were correlated with the time interval after injury in contused skeletal muscle.A panel of 43 endogenous metabolites screened by ultraperformance liquid chromatography coupled with high-resolution mass spectrometry could distinguish the wound ages.The multilayer perceptron (MLP) algorithm exhibited a robust performance in wound age estimation using metabolites.The combination of matabolomics and MLP-MLP tandem model could improve the accuracy of inferring the time interval after injury.
Collapse
Affiliation(s)
| | | | - Jian Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Liangliang Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Kang Ren
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Qiuxiang Du
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Keming Yun
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Yingyuan Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | | |
Collapse
|
4
|
Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J. Deep Learning in Medical Hyperspectral Images: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249790. [PMID: 36560157 PMCID: PMC9784550 DOI: 10.3390/s22249790] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 06/13/2023]
Abstract
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.
Collapse
Affiliation(s)
- Rong Cui
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - He Yu
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Tingfa Xu
- Image Engineering & Video Technology Lab, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
| | - Xiaoxue Xing
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Xiaorui Cao
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Kang Yan
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Jiexi Chen
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| |
Collapse
|
5
|
Pallocci M, Treglia M, Passalacqua P, Luca LD, Zanovello C, Mazzuca D, Guarna F, Gratteri S, Marsella LT. Forensic applications of hyperspectral imaging technique: a narrative review. Med Leg J 2022; 90:216-220. [PMID: 36121069 DOI: 10.1177/00258172221105381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Hyperspectral imaging (HSI) collects and processes information from the entire electromagnetic spectrum to obtain the spectrum of each pixel in the image of a scene, with the aim of finding objects and identifying materials. It is a non-contact, non-destructive technology that can be used without modifying or altering the analysed target. Forensic analysis and crime scene investigations are two of the most investigated fields of application, being able to detect and analyse many types of evidence.In this paper we analysed the most commonly reported forensic science applications.The literature indicates that the fields in which HSI appears most promising are the analysis of blood traces, document forgery, gunshot residues and the identification of fingerprints.
Collapse
Affiliation(s)
- Margherita Pallocci
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Michele Treglia
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Pierluigi Passalacqua
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Lucilla De Luca
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Claudia Zanovello
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| | - Daniela Mazzuca
- Department of Surgical Sciences, University "Magna Græcia" of Catanzaro, 88100, Catanzaro, Italy
| | - Francesca Guarna
- Department of Surgical Sciences, University "Magna Græcia" of Catanzaro, 88100, Catanzaro, Italy
| | - Santo Gratteri
- Department of Surgical Sciences, University "Magna Græcia" of Catanzaro, 88100, Catanzaro, Italy
| | - Luigi T Marsella
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Via Montpellier 1, 00133, Rome, Italy
| |
Collapse
|
6
|
Giulietti N, Discepolo S, Castellini P, Martarelli M. Correction of Substrate Spectral Distortion in Hyper-Spectral Imaging by Neural Network for Blood Stain Characterization. SENSORS (BASEL, SWITZERLAND) 2022; 22:7311. [PMID: 36236410 PMCID: PMC9570875 DOI: 10.3390/s22197311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
In the recent past, hyper-spectral imaging has found widespread application in forensic science, performing both geometric characterization of biological traces and trace classification by exploiting their spectral emission. Methods proposed in the literature for blood stain analysis have been shown to be effectively limited to collaborative surfaces. This proves to be restrictive in real-case scenarios. The problem of the substrate material and color is then still an open issue for blood stain analysis. This paper presents a novel method for blood spectra correction when contaminated by the influence of the substrate, exploiting a neural network-based approach. Blood stains hyper-spectral images deposited on 12 different substrates for 12 days at regular intervals were acquired via a hyper-spectral camera. The data collected were used to train and test the developed neural network model. Starting from the spectra of a blood stain deposited in a generic substrate, the algorithm at first recognizes whether it is blood or not, then allows to obtain the spectra that the same blood stain, at the same time, would have on a reference white substrate with a mean absolute percentage error of 1.11%. Uncertainty analysis has also been performed by comparing the ground truth reflectance spectra with the predicted ones by the neural model.
Collapse
Affiliation(s)
- Nicola Giulietti
- Department of Mechanical Engineering, Politecnico di Milano, 20156 Milano, Italy
| | - Silvia Discepolo
- Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Paolo Castellini
- Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Milena Martarelli
- Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy
| |
Collapse
|
7
|
Nalepa J. Recent Advances in Multi- and Hyperspectral Image Analysis. SENSORS 2021; 21:s21186002. [PMID: 34577211 PMCID: PMC8473276 DOI: 10.3390/s21186002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022]
Abstract
Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span across various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been developing a range of image processing and machine learning analysis pipelines to process these kind of data as efficiently as possible. To this end, multi- or hyperspectral analysis has bloomed and has become an exciting research area which can enable the faster adoption of this technology in practice, also when such algorithms are deployed in hardware-constrained and extreme execution environments; e.g., on-board imaging satellites.
Collapse
Affiliation(s)
- Jakub Nalepa
- Department of Algorithmics and Software, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
| |
Collapse
|
8
|
Zulfiqar M, Ahmad M, Sohaib A, Mazzara M, Distefano S. Hyperspectral Imaging for Bloodstain Identification. SENSORS 2021; 21:s21093045. [PMID: 33925330 PMCID: PMC8123592 DOI: 10.3390/s21093045] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/21/2021] [Accepted: 04/25/2021] [Indexed: 11/16/2022]
Abstract
Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397-1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500-700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.
Collapse
Affiliation(s)
- Maheen Zulfiqar
- Department of Computer Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan; (M.Z.); (A.S.)
| | - Muhammad Ahmad
- Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
- Dipartimento di Matematica e Informatica-MIFT, University of Messina, 98121 Messina, Italy;
- Correspondence:
| | - Ahmed Sohaib
- Department of Computer Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan; (M.Z.); (A.S.)
| | - Manuel Mazzara
- Institute of Software Development and Engineering, Innopolis University, 420500 Innopolis, Russia;
| | - Salvatore Distefano
- Dipartimento di Matematica e Informatica-MIFT, University of Messina, 98121 Messina, Italy;
| |
Collapse
|
9
|
Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks. SENSORS 2021; 21:s21072538. [PMID: 33916361 PMCID: PMC8038564 DOI: 10.3390/s21072538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/31/2021] [Accepted: 04/01/2021] [Indexed: 12/29/2022]
Abstract
The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.
Collapse
|
10
|
He H, Yan S, Lyu D, Xu M, Ye R, Zheng P, Lu X, Wang L, Ren B. Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives. Anal Chem 2021; 93:3653-3665. [PMID: 33599125 DOI: 10.1021/acs.analchem.0c04671] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.
Collapse
Affiliation(s)
- Hao He
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Danya Lyu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Mengxi Xu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Ruiqian Ye
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Peng Zheng
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Xinyu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Lei Wang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| |
Collapse
|
11
|
A dataset for evaluating blood detection in hyperspectral images. Forensic Sci Int 2021; 320:110701. [PMID: 33581656 DOI: 10.1016/j.forsciint.2021.110701] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/11/2021] [Accepted: 01/19/2021] [Indexed: 01/25/2023]
Abstract
The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset, published under an open access license, consists of multiple detection scenarios with varying levels of complexity. It allows to test the performance of Machine Learning methods in relation to different acquisition environments, types of background, age of blood and presence of other blood-like substances. We have explored the dataset with blood detection experiments, for which we have used a hyperspectral target detection algorithm based on the well-known Matched Filter detector. Our results and their discussion highlight the challenges of blood detection in hyperspectral data and form a reference for further works.
Collapse
|