Structural and textural classification of erythrocytes in anaemic cases: A scanning electron microscopic study

Assessment of PVA/silver nanocomposite hydrogel patch as antimicrobial dressing scaffold: Synthesis, characterization and biological evaluation

“…The PVA hydrogel samples were freeze dried and then kept overnight at vacuum chamber to remove excess solvent. Afterwards, the samples were coated with gold in Polaron Gold/Silver Sputter Coating unit [75,76]. The surface morphology of PVA and AgNPs loaded PVA hydrogel were captured in ZEISS EVO Series, Scanning Electron Microscope, Model EVO 50.…”

Artificial Intelligence in Malaria Diagnostics

“…Shan Yang 1, 2 , Yanbing Li , Tuoyu Liu 1, , Haidong Zhang 4 区，RDTs 比血涂片镜检的成本更高 [14] 。因此，目前显微镜检和 RDTs 更多的是相互补充， 而不是相互取代。 与上述两种方法相比，PCR 分子检测方法具有更高的灵敏度和特异性 [7] ，是所有测试 中最准确的方法。PCR 可以检测血液中极低浓度的疟原虫，并能鉴别疟原虫种类。然而， PCR 技术复杂且成本较高，其检测需要经过培训人员进行操作，检测过程需数小时。根据 Tangpukdee 等研究 [7] Otsu 阈值 [15][16][17][18][19][20][21][22][23][24][25][26] 基于图像包含双峰直方图的假设计算最佳阈值 (自适应)直方图阈值 [21,[27][28][29][30][31][32][33][34][35][36][37][38] 难以确定阈值…”

“…Hough 变换 [27,28,37] 需测定血细胞弧度，包括半径、形状 K-均值聚类 [46] 无监督学习技术，使用像素特征描述符通过迭代将像素 分配给 K 聚类 分水岭算法 [22] 提取连续边界区域，但经常出现过度分割 标记控制分水岭算法 [15,23,38,[47][48][49][50][51] 主要用于分割相互接触的细胞 主动轮廓模型 [47,49,[52][53][54] 基于水平集，确保拓扑灵活性，计算量大 基于规则的分割 [55] 需要了解细胞形状、大小、颜色等。 基于模糊规则的分割 [56] 当不确定性较高时，制定规则较困难 模糊发散分割 [57] 神经网络 [58] 需要判别能力较强的特征来区分前景和背景像素 模板匹配 [59] 自适应高斯混合模型距离变换 [60] 距离转换 [61] 自适应提升算法 [62] 查找表 [63] 标准化切割算法 [64] 计算量大 表 2 厚血涂片分割算法 Otsu 阈值 [65] 基于图像可分为两类且遵循双峰直方图的假设，计算最佳阈值 直方图阈值 [44,66,67] 难以确定阈值，通常与其它方法并用以提高性能 形态学运算 [68] 数学形态学运算，包括粒度运算、开运算、闭运算等。用于表征血细胞的弧状外形、 大小、边界、骨架、纹理、渐变等。 模糊 C-均值 [39] 2.…”

“…HSV [20,31,43,46,52,56,64] YcbCr [64] LAB [21,56] 强度 [17,49,54,73] 颜色相关图，颜色共生矩阵 [16] 质地 Haralick [23,27,52] 在不考虑密度 的情况下表征 血细胞的整体 形状和大小。 灰度游程矩阵(GLRLM) [16,27,48,50] GLCM [38,48,50,52,62] 局部二值模式(LBP) [16,48,50,52,72] 分形 [16,50,56] 小波变换 [66] 渐变纹理 [15,18,52,66,73] 灰度共生矩阵 [28,52] 熵 [28,48,50] A c c e p t e d https://engine.scichina.com/doi/10.1360/SSV-2021-0017…”

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

“…Different techniques have been proposed including Otsu thresholding 12 , 38 – 40 and watershed algorithms 41 – 43 that are usually combined with morphology operations to improve segmentation results and address texture complexities; however, improper clump splitting and over-segmentation are the main drawbacks of the methods based on histogram thresholding and watershed transform 7 . To address the splitting of overlapping cells and avoid over segmentation marker-controlled watershed algorithms, 25 , 44 , 45 template matching, 32 , 46 Ada-boost, 31 distance transform, 47 and active contour models 21 , 48 – 50 have been presented, which perform poor to segment highly overlapping cells.…”