People are remarkably accurate (approaching ceiling) at deciding whether faces are male or female, even when cues from hair style, makeup, and facial hair are minimised. Experiments designed to explore the perceptual basis of our ability to categorise the sex of faces are reported. Subjects were considerably less accurate when asked to judge the sex of three-dimensional (3-D) representations of faces obtained by laser-scanning, compared with a condition where photographs were taken with hair concealed and eyes closed. This suggests that cues from features such as eyebrows, and skin texture, play an important role in decision-making. Performance with the laser-scanned heads remained quite high with 3/4-view faces, where the 3-D shape of the face should be easiest to see, suggesting that the 3-D structure of the face is a further source of information contributing to the classification of its sex. Performance at judging the sex from photographs (with hair concealed) was disrupted if the photographs were inverted, which implies that the superficial cues contributing to the decision are not processed in a purely 'local' way. Performance was also disrupted if the faces were shown in photographic negatives, which is consistent with the use of 3-D information, since negation probably operates by disrupting the computation of shape from shading. In 3-D, the 'average' male face differs from the 'average' female face by having a more protuberant nose/brow and more prominent chin/jaw. The effects of manipulating the shapes of the noses and chins of the laser-scanned heads were assessed and significant effects of such manipulations on the apparent masculinity or femininity of the heads were revealed. It appears that our ability to make this most basic of facial categorisations may be multiply determined by a combination of 2-D, 3-D, and textural cues and their interrelationships.

No abstract

BackgroundSemiquantitative evaluation and manual cell counting are the commonly used procedures to assess positive staining of molecular markers in tissue sections. Manual counting is also a laborious task in which consistent objectivity is difficult to achieve. Recently, image analysis has been explored, but the studies reported were limited to histological images acquired at high magnification and containing uniformly stained cells.MethodsThe analyzed material consisted of histological sections from different squamous cell cancers that had stained for proliferation using Ki‐67 and cyclin A detection. The first step of the method was based on detecting the overall number of cells irrespective to their stain, using second‐order edge detection methodology. Then proliferating cells were located using principal component analysis (PCA) of the color image, combined with histogram thresholding.ResultsThe algorithms' performances were validated on tissue section images encountered in routine clinical practice by comparison with objective measures of performance and manual cell identification. The algorithms correlated closely with manual counting of all cells (r2 = 0.96–0.97) and stained cells (4–7% cell count error).ConclusionsCell counting in complex large‐scale histological images could be applied in routine practice using edge and color information. The proposed technique provides several benefits, such as speed of analysis, consistency, and automation. Moreover, it is faster than human observation and could replace the laborious task of manual cell counting. Cytometry Part A 55A:30–42, 2003. © 2003 Wiley‐Liss, Inc.

No abstract