Implementation of Hyperspectral Technology in Skin Detection: Building Efficient Systems and Robust Models

在上一篇文章中，我們探討了高光譜成像技術在皮膚檢測中的潛力，而本文將關注如何實現這一技術的實現。

In the previous article, we explored the potential of hyperspectral imaging technology in skin detection. This article will focus on its practical implementation.

皮膚樣品的可見光和近紅外光譜 / Visible and Near-Infrared Spectra of Skin Samples

為實現高光譜成像技術的有效應用，多個研究團隊搭建了各具特色的高光譜成像系統。其中一個西班牙團隊，搭建了不同的系統。他們使用398.08~995.20nm的高光譜相機，配備了電動底座和鹵素光源，以優化成像質量，確保穩定的數據采集。

該團隊還搭建了，采用900~1700nm的光譜范圍，搭建系統時特別關注患者的舒適度，設計了支撐裝置，讓患者在拍攝過程中能夠穩定休息。此裝置由金屬梁和多個3D打印支撐平臺構成，提供了柔軟且適應不同部位的支持。

To effectively apply hyperspectral imaging, multiple research teams have developed specialized systems. One Spanish team, for instance, constructed distinct setups. They employed a hyperspectral camera covering 398.08–995.20 nm, equipped with a motorized stage and halogen lighting to optimize imaging quality and ensure stable data acquisition.

The team also developed another system operating in the 900–1700 nm range, prioritizing patient comfort by incorporating a support device that allowed subjects to remain stable during imaging. This setup consisted of metal beams and multiple 3D-printed support platforms, providing soft and adaptable positioning for different body areas.

可見光系統 / the visible light system

近紅外系統。（a）本研究中為數據采集目的而構建的高光譜推掃平臺。（b）在采集過程中幫助患者感到舒適的不同支持平臺。

the near-infrared system. (a) The hyperspectral push-broom platform constructed for data collection in this study. (b) Various support platforms designed to enhance patient comfort during acquisition.

在數據分析方法上，近年來的研究主要集中在機器學習模型的應用。傳統的簡單圖像處理方法雖然實現直接，但在應對復雜皮膚病變時，其效果往往不能令人滿意。機器學習模型，為皮膚檢測的準確性提供了支持，這些模型具備良好的泛化能力，能夠在多種條件下有效識別不同類型的皮膚病變。

Recent research has increasingly focused on machine learning models for data analysis. While traditional image processing methods are straightforward, their performance in detecting complex skin lesions is often unsatisfactory. Machine learning models, however, offer superior accuracy and generalization, enabling reliable identification of diverse skin lesions under varying conditions.

在一個研究中，科研人員對不同的分類和分割方法進行了比較。這些方法各有優缺點，支持向量機在高維空間中表現良好，隨機森林對過擬合有一定的魯棒性，K均值聚類適用于簡單的分類任務，而主成分分析則有效進行降維，保留數據中的重要特征。這具體取決于組織和目標病變的類型。

In one study, researchers compared different classification and segmentation approaches, each with unique strengths:

·Support Vector Machines (SVM) excel in high-dimensional spaces.

·Random Forests demonstrate robustness against overfitting.

·K-means Clustering is suitable for simpler classification tasks.

·Principal Component Analysis (PCA) effectively reduces dimensionality while preserving critical features.

·The optimal method depends on tissue type and the target lesion.

各類方法的比較（部分）/ Comparison of different methodologies (partial)

前面提到的西班牙團隊，該團隊利用近紅外高光譜成像技術，針對基底細胞癌（BCC）和皮膚鱗狀細胞癌（SCC）進行了檢測，強調使用魯棒特征統計方法來進行數據分析。該方法不僅提高了系統的穩定性，還確保在樣本中存在噪聲和異常值時，依舊能獲得較高的檢測準確性。

the aforementioned Spanish team utilized near-infrared hyperspectral imaging to detect basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), emphasizing robust statistical feature extraction. This approach not only improved system stability but also maintained high detection accuracy despite noise and outliers.

上圖：使用每個樣本在各個波長上的中位數值所得到的魯棒特征。

下圖：使用平方根的雙權重中方差作為每個樣本變異度的測量方法，得到這些樣本的魯棒偏差。

Top: Robust features derived from median values of each sample across wavelengths.

Bottom: Robust deviations calculated using the square root of the biweight midvariance (√BWMV) as a measure of variability.

此外，他們在另一個實驗中重點關注BCC、SCC和AK（光化性角化病）與健康皮膚的差異，同樣采用了魯棒統計方法，同時還使用多變量統計分析進行樣本間的比較，以發現數據中潛在的差異。

In another experiment, the team examined differences among BCC, SCC, actinic keratosis (AK), and healthy skin, again applying robust statistics alongside multi-variate analysis to uncover subtle data variations.

在本研究中通過多種方法確定的最佳定義窗口。虛線垂直線所劃定的區域標記了573.45nm至779.88nm之間最終感興趣的窗口。

Optimal spectral window (573.45–779.88 nm, marked by dashed vertical lines) identified through multiple methods in this study.

每個樣本的高光譜特征。（a）魯棒特征標記了中央傾向以及5%和95%百分位置信區間（下線和上線分別）。（b）√BWMV計算表示魯棒樣本方差。

Hyperspectral features of each sample. (a) Robust features indicating central tendency with 5% and 95% percentile confidence intervals (lower and upper bounds, respectively). (b) √BWMV representing robust sample variance.

綜上所述，高光譜成像技術在皮膚檢測中展現出了優勢，尤其是在系統構建與模型泛化能力方面。通過選擇適宜的波長范圍，結合先進的數據分析技術，我們的高光譜相機在皮膚疾病早期檢測中提供了堅實的基礎。

值得一提的是，我們公司不僅銷售高光譜相機，還能提供專業的硬件技術支持，助力您的研究與應用提升效率。未來，隨著高光譜成像技術與機器學習的深度融合，該領域必將迎來更多機會，相信皮膚癌的早期檢測將變得更加高效和可靠，為患者帶來更大的福音。

Hyperspectral imaging demonstrates unique advantages in skin detection, particularly in system design and model generalization. By selecting optimal wavelength ranges and integrating advanced analytics, hyperspectral cameras provide a robust foundation for early skin disease diagnosis.

Notably, our company not only supplies hyperspectral cameras but also offers expert hardware support to enhance research and application efficiency. As hyperspectral imaging and machine learning continue to converge, this field holds immense promise—ushering in more efficient, reliable early detection of skin cancer and greater benefits for patients.

案例來源 / Source：

1. Courtenay LA, González-Aguilera D, Lagüela S, Del Pozo S, Ruiz-Mendez C, Barbero-García I, Román-Curto C, Ca?ueto J, Santos-Durán C, Carde?oso-álvarez ME, Roncero-Riesco M, Hernandez-Lopez D, Guerrero-Sevilla D, Rodríguez-Gonzalvez P. Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis. Biomed Opt Express. 2021 Jul 20;12(8):5107-27. doi: 10.1364/BOE.428143. PMID: 34513245; PMCID: PMC8407807.

2. Courtenay LA, Barbero-García I, Martínez-Lastras S, Del Pozo S, Corral de la Calle M, Garrido A, Guerrero-Sevilla D, Hernandez-Lopez D, González-Aguilera D. Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation. PLoS One. 2024 Apr 25;19(4):e0300400. doi: 10.1371/journal.pone.0300400. PMID: 38662718; PMCID: PMC11045066.

3. Aloupogianni E, Ishikawa M, Kobayashi N, Obi T. Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review. J Biomed Opt. 2022 Jun 8;27(6):060901. doi: 10.1117/1.JBO.27.6.060901.