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公开(公告)号：US07512277B2
公开(公告)日：2009-03-31
申请号：US10510649
申请日：2003-04-09
CPC分类号： H04N19/167 , H04N19/115 , H04N19/146 , H04N19/162 , H04N19/17 , H04N19/186 , H04N19/61 , H04N19/63
摘要： The invention describes a wavelet compression method for color converted image planes (Y, Cb and Cr), wherein a relatively low (e.g. zero) degree of compression is performed in areas of an original color image having relatively higher importance, a relatively higher degree of compression in those areas indicated to be of lower importance. It performs a hierarchical encoding of a reduced wavelet image by discarding wavelet coefficients which satisfy the two criteria of firstly corresponding to image areas of relatively lower importance and secondly being below a certain wavelet coefficient threshold. The wavelet coefficient threshold is determined from a calculation histogram to remove a percentage of the image and can be specified as an input parameter by a user.
摘要翻译： 本发明描述了一种用于彩色转换图像平面（Y，Cb和Cr）的小波压缩方法，其中在具有相对较高重要性的原始彩色图像的区域中执行相对低（例如零）压缩程度，相对较高程度的 这些地区的压缩表现为较低的重要性。 它通过丢弃满足首先对应于相对较低重要性的图像区域的二个标准并且其次低于某一小波系数阈值的小波系数来执行缩小的小波图像的分层编码。 从计算直方图中确定小波系数阈值以去除图像的百分比，并且可以由用户指定为输入参数。
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公开(公告)号：US20080273787A1
公开(公告)日：2008-11-06
申请号：US12065793
申请日：2006-09-04
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T7/90 , G06T2207/10056 , G06T2207/20021 , G06T2207/20036 , G06T2207/30024 , G06T2207/30096
摘要： A method for automated selection of image regions (“tiles”) in an overall image includes computing a gray-level co-occurrence matrix and entropy of a green plane of the overall image, applying morphological closing to the matrix and thresholding the matrix and entropy image to provide binary masks. The matrix and entropy masks are combined with a vignette mask, the combination indicating areas of acceptable tissue from which tiles are selected randomly. For cancer grading; image data is transformed to Hue, Saturation and Value; for steroid/protein expression analysis it is transformed to cyan and a Sobel of cyan is computed. A feature measure is computed for each tile based on colour and texture, and is carried out randomly but influenced by feature measure. Finally, from the further selection, tiles are chosen which combine high feature measure with low overlap.
摘要翻译： 用于在整个图像中自动选择图像区域（“瓦片”）的方法包括计算整个图像的绿色平面的灰度共生矩阵和熵，向矩阵应用形态闭合并且对矩阵和熵进行阈值化 图像提供二进制掩码。 矩阵和熵掩模与晕影掩模组合，指示随机选择瓦片的可接受组织的区域的组合。 对于癌症分级; 图像数据被转换为色相，饱和度和价值; 对于类固醇/蛋白质表达分析，将其转化为青色并计算青色的Sobel。 基于颜色和纹理为每个瓦片计算特征度量，并且随机地执行，但受特征测量的影响。 最后，从进一步的选择中，选择将高特征度量与低重叠相结合的瓦片。
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公开(公告)号：US08265370B2
公开(公告)日：2012-09-11
申请号：US12065793
申请日：2006-09-04
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06T7/90 , G06T2207/10056 , G06T2207/20021 , G06T2207/20036 , G06T2207/30024 , G06T2207/30096
摘要： A method for automated selection of image regions (“tiles”) in an overall image includes computing a gray-level co-occurrence matrix and entropy of a green plane of the overall image, applying morphological closing to the matrix and thresholding the matrix and entropy image to provide binary masks. The matrix and entropy masks are combined with a vignette mask, the combination indicating areas of acceptable tissue from which tiles are selected randomly. For cancer grading; image data is transformed to Hue, Saturation and Value; for steroid/protein expression analysis it is transformed to cyan and a Sobel of cyan is computed. A feature measure is computed for each tile based on color and texture, and is carried out randomly but influenced by feature measure. Finally, from the further selection, tiles are chosen which combine high feature measure with low overlap.
摘要翻译： 用于在整个图像中自动选择图像区域（“瓦片”）的方法包括计算整个图像的绿色平面的灰度共生矩阵和熵，向矩阵应用形态闭合并且对矩阵和熵进行阈值化 图像提供二进制掩码。 矩阵和熵掩模与晕影掩模组合，指示随机选择瓦片的可接受组织的区域的组合。 对于癌症分级; 图像数据被转换为色相，饱和度和价值; 对于类固醇/蛋白质表达分析，将其转化为青色并计算青色的Sobel。 基于颜色和纹理为每个瓦片计算特征度量，并且随机地执行，但受特征测量的影响。 最后，从进一步的选择中，选择将高特征度量与低重叠相结合的瓦片。
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公开(公告)号：US08229674B2
公开(公告)日：2012-07-24
申请号：US10529509
申请日：2003-10-20
IPC分类号： G01N33/50
CPC分类号： G06K9/38 , G06K9/0014 , G06T7/0012 , G06T7/62 , G06T2207/30024
摘要： A method of grading tubules in a first histological slide image derives a second image of objects in the first image of objects in the first image with boundary characteristics corresponding to tubules. It also derives a third image of second objects in the first image having pixel value characteristic of fat and holes within tubules. It combines data from the second and third images to identify holes within tubules and determines the relative areas of holes as proportions of their tubules to provide ratios, individual tubule ratios and an overall ration for all holes and tubules collectively. The number of tubules containing appreciably sized holes is counted. Tubules are graded by thresholding based on individual and overall tubule/hole area ratios, tubule/object proportion, tubule number and number of tubule with appreciably sized holes. Thresholds are derived from image gradation by an appropriate medical expert.
摘要翻译： 在第一组织学幻灯片图像中分级小管的方法在第一图像中的对象的第一图像中的对象的第二图像获得具有对应于小管的边界特征。 它还导出第一图像中的第二个物体的第三个图像，其具有小管内的脂肪和孔的特征的像素值。 它结合了来自第二和第三图像的数据以识别小管内的孔，并确定孔的相对面积作为其小管的比例，以提供比例，单个小管比率以及所有孔和小管的总体比例。 计数含有明显大小的孔的小管的数量。 小管根据个体和整体小管/孔面积比，小管/物体比例，小管数量和具有明显大小的孔的小管数量进行阈值分级。 阈值由适当的医学专家从图像灰度中导出。
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5.发明授权Scoring estrogen and progesterone receptors expression based on image analysis 有权基于图像分析评估雌激素和孕酮受体表达公开(公告)号：US07646905B2
公开(公告)日：2010-01-12
申请号：US10540444
申请日：2003-12-16
CPC分类号： G06T7/0012
摘要： A method of scoring Oestrogen and Progesterone Receptors expression (ER and PR) from histological images determines the percentage of brown image blob area in total blob area and derives percentage thresholds to quantify scoring. Brown blob area proportion is then compared with the thresholds to provide a first contribution to scoring of ER or PR. The number of relatively dark pixels is then counted, pixel number thresholds are determined to quantify scoring, and the number of relatively dark pixels is compared with the thresholds to provide a second contribution scoring of ER or PR. The two contributions are then added to provide an overall scoring in the range 0 to 8 which may be taken as a final score or it may be scaled to a conventional range 0 to 3.
摘要翻译： 从组织学图像中评估雌激素和孕激素受体表达（ER和PR）的方法决定了棕色图像斑点面积在总斑点面积中的百分比，并得出百分比阈值以量化评分。 然后将棕色斑点面积比例与阈值进行比较，为ER或PR的评分提供第一个贡献。 然后对相对较暗的像素的数量进行计数，确定像素数阈值以量化评分，并将相对较暗的像素的数量与阈值进行比较以提供ER或PR的第二贡献评分。 然后添加两个贡献以提供0至8范围内的总体评分，其可以被视为最终得分，或者可以缩放到常规范围0至3。
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公开(公告)号：US07616790B2
公开(公告)日：2009-11-10
申请号：US10535323
申请日：2003-11-13
CPC分类号： G06K9/0014 , G06T7/0012 , G06T7/62
摘要： A method of histological assessment of nuclear pleomorphism to identify potential cell nuclei divides image data into overlapping sub-images. It uses principal component analysis to derive monochromatic image data, followed by Otsu thresholding to produce a binary image. It removes image regions at sub-image boundaries, unsuitably small image regions and holes in relatively large image regions. It then reassembles the resulting sub-images into a single image. Perimeters (P) and areas (A) of image regions which are potential cell nuclei are determined and used in calculating nuclear shape factors P2/A. Nuclear pleomorphism is assessed as relatively low, moderate or high according to whether predetermined shape factor thresholds indicate a mean cell nucleus shape factor for an image is relatively low, moderate or high.
摘要翻译： 核多型的组织学评估方法，以识别潜在的细胞核，将图像数据划分为重叠的子图像。 它使用主成分分析来导出单色图像数据，其次是Otsu阈值以产生二值图像。 它在子图像边界去除图像区域，在相对较大的图像区域中移除不适当的小图像区域和孔。 然后将所生成的子图像重新组合成单个图像。 确定潜在细胞核的图像区域的周长（P）和面积（A），并用于计算核形状因子P2 / A。 根据预定形状因子阈值是否表示图像的平均细胞核形状因子相对较低，中等或高，核多形性被评估为相对低，中等或高。
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公开(公告)号：US07577280B2
公开(公告)日：2009-08-18
申请号：US10535140
申请日：2003-11-13
申请人： Christelle Marie Guittet , Paul Gerard Ducksbury , Maria Petrou , Anastasios Kesidis , Roberto Cipolla , Margaret Jai Varga
发明人： Christelle Marie Guittet , Paul Gerard Ducksbury , Maria Petrou , Anastasios Kesidis , Roberto Cipolla , Margaret Jai Varga
CPC分类号： G06K9/00127 , G01N15/1475 , G06T7/0012 , G06T2207/30024
摘要： A method of measurement of mitotic activity from histopathological specimen images initially identifies image pixels with luminances corresponding to mitotic figures and selects from them a reference pixel to provide a reference color. Pixels similar to the reference color are located; image regions are grown on located pixels by adding pixels satisfying thresholds of differences to background and image region luminances. Grown regions are thresholded in area, compactness, width/height ratio, luminance ratio to background and difference between areas grown with perturbed thresholds. Grown regions are counted as indicating mitotic figures by thresholding region number, area and luminance. An alternative method of measuring mitotic activity measures a profile of an image region and counts the image region as corresponding to a mitotic figure if its profile is above a threshold at an intensity associated with mitotic figures. A mitotic figure is also indicated if the profile does not meet the previous criterion but has three other values satisfying respective threshold criteria.
摘要翻译： 从组织病理学标本图像中测量有丝分裂活性的方法最初识别具有对应于有丝分裂图的亮度的图像像素，并从中选择参考像素以提供参考色。 位于与参考色相似的像素; 通过将满足不同阈值的像素添加到背景和图像区域亮度，在位置的像素上生长图像区域。 生长区域在面积，紧凑度，宽度/高度比，背景亮度比和扰动阈值生长区域之间的差异被阈值化。 通过阈值区域数量，面积和亮度将生长区域计数为指示有丝分裂图。 测量有丝分裂活性的替代方法测量图像区域的轮廓，并且如果其分布在与有丝分裂图相关联的强度下高于阈值，则将图像区域计数为对应于有丝分裂图。 如果轮廓不符合先前的标准，但是还有三个其他值满足相应的阈值标准，则还会显示有丝分裂图。
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